From 4dc61b57d0234a149e217224332568644b6468ba Mon Sep 17 00:00:00 2001
From: dme65 <dme65@cornell.edu>
Date: Tue, 11 Dec 2018 15:36:59 -0500
Subject: [PATCH 01/25] Working on GPs with derivative observations.

---
 gpytorch/kernels/__init__.py         |  2 +
 gpytorch/kernels/rbf_kernel_grad.py  | 99 ++++++++++++++++++++++++++++
 gpytorch/means/__init__.py           |  3 +-
 gpytorch/means/constant_mean_grad.py | 19 ++++++
 4 files changed, 122 insertions(+), 1 deletion(-)
 create mode 100644 gpytorch/kernels/rbf_kernel_grad.py
 create mode 100644 gpytorch/means/constant_mean_grad.py

diff --git a/gpytorch/kernels/__init__.py b/gpytorch/kernels/__init__.py
index 1610a9845..7ee10c48e 100644
--- a/gpytorch/kernels/__init__.py
+++ b/gpytorch/kernels/__init__.py
@@ -15,6 +15,7 @@
 from .periodic_kernel import PeriodicKernel
 from .product_structure_kernel import ProductStructureKernel
 from .rbf_kernel import RBFKernel
+from .rbf_kernel_grad import RBFKernelGrad
 from .scale_kernel import ScaleKernel
 from .spectral_mixture_kernel import SpectralMixtureKernel
 from .white_noise_kernel import WhiteNoiseKernel
@@ -38,6 +39,7 @@
     "ProductKernel",
     "ProductStructureKernel",
     "RBFKernel",
+    "RBFKernelGrad",
     "ScaleKernel",
     "SpectralMixtureKernel",
     "WhiteNoiseKernel",
diff --git a/gpytorch/kernels/rbf_kernel_grad.py b/gpytorch/kernels/rbf_kernel_grad.py
new file mode 100644
index 000000000..595ab7f98
--- /dev/null
+++ b/gpytorch/kernels/rbf_kernel_grad.py
@@ -0,0 +1,99 @@
+#!/usr/bin/env python3
+from .rbf_kernel import RBFKernel
+from ..lazy import DiagLazyTensor, NonLazyTensor
+from ..utils.deprecation import _deprecate_kwarg
+from torch.nn.functional import softplus
+import torch
+
+
+class RBFKernelGrad(RBFKernel):
+    def __init__(
+        self,
+        ard_num_dims=None,
+        batch_size=1,
+        active_dims=None,
+        lengthscale_prior=None,
+        param_transform=softplus,
+        inv_param_transform=None,
+        eps=1e-6,
+        **kwargs
+    ):
+        # TODO: Add support for ARD
+        if ard_num_dims is not None:
+            raise RuntimeError('ARD is not supported with derivative observations yet!')
+            
+        super(RBFKernelGrad, self).__init__(
+            ard_num_dims=None,
+            batch_size=1,
+            active_dims=None,
+            lengthscale_prior=None,
+            param_transform=softplus,
+            inv_param_transform=None,
+            eps=1e-6,
+            **kwargs
+        )
+
+    def forward(self, x1, x2, diag=False, **params):
+        _, n, d = x1.size()
+        if not diag:
+            K = torch.zeros(n*(d+1), n*(d+1))
+            x1_ = x1.div(self.lengthscale)
+            x2_ = x2.div(self.lengthscale)
+            x1_, x2_ = self._create_input_grid(x1_, x2_, **params)
+
+            all_diff = (x1_ - x2_)
+            diff = all_diff.norm(2, dim=-1)
+
+            # 1) Kernel block
+            K_11 = diff.pow(2).div(-2).exp()
+            K[:n, :n] = K_11
+
+            shape_list = [1] * len(K_11.shape[:-2])
+            left_shape_list = shape_list + [d, 1]
+            right_shape_list = shape_list + [1, d]
+
+            # 2) Gradient block
+            print(K_11.shape)
+            K_rep = K_11.unsqueeze(-1).repeat(*(shape_list + [1, 1, d]))
+            all_K = (all_diff / self.lengthscale) * K_rep
+            deriv_K = all_K.transpose(-3, -1).transpose(-2, -1).contiguous().view(d * n, n)
+
+            K[:n, n:] = deriv_K.t()
+            K[n:, :n] = deriv_K
+            
+            # 3) Hessian block
+
+            outer_prod = all_diff.transpose(-3, -1).transpose(-2, -1).contiguous().view(d * n, n)
+            outer_prod = outer_prod.repeat(*right_shape_list)
+
+            repeated_K = K_11.repeat(*left_shape_list).repeat(*right_shape_list)
+
+            diag_part = DiagLazyTensor(NonLazyTensor(repeated_K).diag()).evaluate()
+            diag_part = diag_part / self.lengthscale.pow(2)
+
+            K[n:, n:] = diag_part - (outer_prod / self.lengthscale.pow(2)) * repeated_K
+
+            pi = torch.arange(n * (d + 1)).view(d + 1, n).t().contiguous().view((n * (d + 1)))
+            K = K[pi, :][:, pi]
+
+            return K
+        else:
+            kernel_diag = super(RBFKernelGrad, self).forward(x1, x2, diag=True)
+            # TODO: This will change when ARD is supported
+            grad_diags = (1 / self.lengthscale.squeeze().pow(2)).expand_as(kernel_diag).repeat(1, d)
+
+            k_diag = torch.cat((kernel_diag, grad_diags), dim=-1)
+            pi = torch.arange(n * (d + 1)).view(d + 1, n).t().contiguous().view((n * (d + 1)))
+            return k_diag[pi]
+
+
+    def size(self, x1, x2):
+        """
+        Given `n` data points in `d` dimensions, RBFKernelGrad returns an `n(d+1) x n(d+1)` kernel
+        matrix.
+        """
+        non_batch_size = ((x1.size(-1)+1) * x1.size(-2), (x2.size(-1)+1) * x2.size(-2))
+        if x1.ndimension() == 3:
+            return torch.Size((x1.size(0),) + non_batch_size)
+        else:
+            return torch.Size(non_batch_size)
\ No newline at end of file
diff --git a/gpytorch/means/__init__.py b/gpytorch/means/__init__.py
index 7733f9758..3c9a7eff3 100644
--- a/gpytorch/means/__init__.py
+++ b/gpytorch/means/__init__.py
@@ -2,7 +2,8 @@
 
 from .mean import Mean
 from .constant_mean import ConstantMean
+from .constant_mean_grad import ConstantMeanGrad
 from .multitask_mean import MultitaskMean
 from .zero_mean import ZeroMean
 
-__all__ = ["Mean", "ConstantMean", "MultitaskMean", "ZeroMean"]
+__all__ = ["Mean", "ConstantMean", "ConstantMeanGrad", "MultitaskMean", "ZeroMean"]
diff --git a/gpytorch/means/constant_mean_grad.py b/gpytorch/means/constant_mean_grad.py
new file mode 100644
index 000000000..0d4c2a9ce
--- /dev/null
+++ b/gpytorch/means/constant_mean_grad.py
@@ -0,0 +1,19 @@
+#!/usr/bin/env python3
+
+import torch
+from .mean import Mean
+
+
+class ConstantMeanGrad(Mean):
+    def __init__(self, prior=None, batch_size=None):
+        super(ConstantMeanGrad, self).__init__()
+        self.batch_size = batch_size
+        self.register_parameter(name="constant", parameter=torch.nn.Parameter(torch.zeros(batch_size or 1, 1)))
+        if prior is not None:
+            self.register_prior("mean_prior", prior, "constant")
+
+    def forward(self, input):
+        mean = self.constant.unsqueeze(-1).repeat(input.size(0), input.size(1), input.size(2) + 1)
+        mean[..., :, 1:] = 0
+        return mean
+    

From ab650697b5555569f1d0f2ded7fb3b8d8d9f6bfa Mon Sep 17 00:00:00 2001
From: Jake Gardner <gardner.jake@gmail.com>
Date: Tue, 11 Dec 2018 15:58:37 -0500
Subject: [PATCH 02/25] WIP

---
 examples/Simple_GP_Derivative_Example.ipynb | 451 ++++++++++++++++++++
 gpytorch/kernels/rbf_kernel_grad.py         |  25 +-
 2 files changed, 463 insertions(+), 13 deletions(-)
 create mode 100644 examples/Simple_GP_Derivative_Example.ipynb

diff --git a/examples/Simple_GP_Derivative_Example.ipynb b/examples/Simple_GP_Derivative_Example.ipynb
new file mode 100644
index 000000000..a38bcfbb7
--- /dev/null
+++ b/examples/Simple_GP_Derivative_Example.ipynb
@@ -0,0 +1,451 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import gpytorch\n",
+    "import math\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_x = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "\n",
+    "train_y = torch.stack([\n",
+    "    torch.sin(train_x * (2 * math.pi)),\n",
+    "    torch.cos(train_x * (2 * math.pi)),\n",
+    "], -1).squeeze(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class GPModelWithDerivatives(gpytorch.models.ExactGP):\n",
+    "    def __init__(self, train_x, train_y, likelihood):\n",
+    "        super(GPModelWithDerivatives, self).__init__(train_x, train_y, likelihood)\n",
+    "        self.mean_module = gpytorch.means.ConstantMeanGrad()\n",
+    "        self.covar_module = gpytorch.kernels.RBFKernelGrad()\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        mean_x = self.mean_module(x)\n",
+    "        covar_x = self.covar_module(x)\n",
+    "        return gpytorch.distributions.MultitaskMultivariateNormal(mean_x, covar_x)\n",
+    "\n",
+    "    \n",
+    "likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihoodKronecker(num_tasks=train_x.size(-1))\n",
+    "model = GPModelWithDerivatives(train_x, train_y, likelihood)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 200, 200])\n",
+      "Iter 1/50 - Loss: 111.286\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 2/50 - Loss: 125.321\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 3/50 - Loss: 258.181\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 4/50 - Loss: 174.509\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 5/50 - Loss: 480.031\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 6/50 - Loss: 205.248\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 7/50 - Loss: 881.470\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 8/50 - Loss: 1699.690\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 9/50 - Loss: 1286.258\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 10/50 - Loss: 5270.967\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 11/50 - Loss: 9693.424\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 12/50 - Loss: 6059.958\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 13/50 - Loss: -5199.472\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 14/50 - Loss: -170.532\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 15/50 - Loss: -6329.444\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 16/50 - Loss: -135065.984\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 17/50 - Loss: 206.190\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 18/50 - Loss: 239.457\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 19/50 - Loss: 205.388\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 20/50 - Loss: 140.232\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 21/50 - Loss: 132.896\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 22/50 - Loss: 141.811\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 23/50 - Loss: 137.427\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 24/50 - Loss: 138.412\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 25/50 - Loss: 138.847\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 26/50 - Loss: 143.090\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 27/50 - Loss: 142.166\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 28/50 - Loss: 135.707\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 29/50 - Loss: 122.478\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 30/50 - Loss: 136.192\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 31/50 - Loss: 133.821\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 32/50 - Loss: 138.390\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 33/50 - Loss: 55.152\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 34/50 - Loss: -366.331\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 35/50 - Loss: -8708.603\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 36/50 - Loss: 346347.875\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 37/50 - Loss: 143.309\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 38/50 - Loss: 110.308\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 39/50 - Loss: 61.255\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 40/50 - Loss: 145.943\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 41/50 - Loss: 140.922\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 42/50 - Loss: 133.338\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 43/50 - Loss: 146.797\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 44/50 - Loss: 178.403\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 45/50 - Loss: 200.601\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 46/50 - Loss: 261.491\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 47/50 - Loss: 476.087\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 48/50 - Loss: 1569.892\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 49/50 - Loss: 39871.793\n",
+      "torch.Size([1, 200, 200])\n",
+      "Iter 50/50 - Loss: -8705.504\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Find optimal model hyperparameters\n",
+    "model.train()\n",
+    "likelihood.train()\n",
+    "\n",
+    "# Use the adam optimizer\n",
+    "optimizer = torch.optim.Adam([\n",
+    "    {'params': model.parameters()},  # Includes GaussianLikelihood parameters\n",
+    "], lr=0.1)\n",
+    "\n",
+    "# \"Loss\" for GPs - the marginal log likelihood\n",
+    "mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)\n",
+    "\n",
+    "n_iter = 50\n",
+    "for i in range(n_iter):\n",
+    "    optimizer.zero_grad()\n",
+    "    output = model(train_x)\n",
+    "    loss = -mll(output, train_y)\n",
+    "    loss.backward()\n",
+    "    print('Iter %d/%d - Loss: %.3f' % (i + 1, n_iter, loss.item()))\n",
+    "    optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 200, 200])\n"
+     ]
+    }
+   ],
+   "source": [
+    "covar = gpytorch.kernels.RBFKernelGrad()\n",
+    "K = covar(train_x).evaluate_kernel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[ 1.0000,  0.0000,  0.9999,  ...,  0.7431,  0.3532,  0.7352],\n",
+       "        [ 0.0000,  2.0814, -0.0210,  ...,  1.0721, -0.7352,  1.0606],\n",
+       "        [ 0.9999, -0.0210,  1.0000,  ...,  0.7509,  0.3607,  0.7431],\n",
+       "        ...,\n",
+       "        [ 0.7431, -1.0721,  0.7509,  ...,  2.0814, -0.0210,  0.0303],\n",
+       "        [ 0.3532, -0.7352,  0.3607,  ..., -0.0210,  1.0000,  0.0000],\n",
+       "        [ 0.7352, -1.0606,  0.7431,  ..., -0.0303,  0.0000,  2.0814]],\n",
+       "       grad_fn=<SelectBackward>)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "K.evaluate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 200, 200])\n",
+      "torch.Size([1, 200, 200])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set into eval mode\n",
+    "model.eval()\n",
+    "likelihood.eval()\n",
+    "\n",
+    "# Initialize plots\n",
+    "f, (y1_ax, y2_ax) = plt.subplots(1, 2, figsize=(8, 3))\n",
+    "\n",
+    "# Make predictions\n",
+    "with torch.no_grad(), gpytorch.fast_pred_var():\n",
+    "    test_x = train_x\n",
+    "    predictions = likelihood(model(test_x))\n",
+    "    mean = predictions.mean\n",
+    "    lower, upper = predictions.confidence_region()\n",
+    "    \n",
+    "# This contains predictions for both tasks, flattened out\n",
+    "# The first half of the predictions is for the first task\n",
+    "# The second half is for the second task\n",
+    "\n",
+    "# Plot training data as black stars\n",
+    "y1_ax.plot(train_x.detach().numpy(), train_y[:, 0].detach().numpy(), 'k*')\n",
+    "# Predictive mean as blue line\n",
+    "y1_ax.plot(test_x.numpy(), mean[:, 0].numpy(), 'b')\n",
+    "# Shade in confidence \n",
+    "y1_ax.fill_between(test_x.squeeze().numpy(), lower[:, 0].numpy(), upper[:, 0].numpy(), alpha=0.5)\n",
+    "y1_ax.set_ylim([-3, 3])\n",
+    "y1_ax.legend(['Observed Data', 'Mean', 'Confidence'])\n",
+    "y1_ax.set_title('Observed Values (Likelihood)')\n",
+    "\n",
+    "# Plot training data as black stars\n",
+    "y2_ax.plot(train_x.detach().numpy(), train_y[:, 1].detach().numpy(), 'k*')\n",
+    "# Predictive mean as blue line\n",
+    "y2_ax.plot(test_x.numpy(), mean[:, 1].numpy(), 'b')\n",
+    "# Shade in confidence \n",
+    "y2_ax.fill_between(test_x.squeeze().numpy(), lower[:, 1].numpy(), upper[:, 1].numpy(), alpha=0.5)\n",
+    "y2_ax.set_ylim([-3, 3])\n",
+    "y2_ax.legend(['Observed Data', 'Mean', 'Confidence'])\n",
+    "y2_ax.set_title('Observed Values (Likelihood)')\n",
+    "\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[  5778.8516,  19794.4297],\n",
+       "        [  5714.1924,  19636.9375],\n",
+       "        [  5646.6792,  19476.7695],\n",
+       "        [  5576.3120,  19313.9375],\n",
+       "        [  5503.0776,  19148.4453],\n",
+       "        [  5426.9751,  18980.2988],\n",
+       "        [  5347.9956,  18809.5039],\n",
+       "        [  5266.1445,  18636.0625],\n",
+       "        [  5181.4106,  18459.9727],\n",
+       "        [  5093.7974,  18281.2305],\n",
+       "        [  5003.2998,  18099.8320],\n",
+       "        [  4909.9219,  17915.7695],\n",
+       "        [  4813.6606,  17729.0391],\n",
+       "        [  4714.5283,  17539.6270],\n",
+       "        [  4612.5186,  17347.5215],\n",
+       "        [  4507.6392,  17152.7129],\n",
+       "        [  4399.8940,  16955.1836],\n",
+       "        [  4289.2969,  16754.9219],\n",
+       "        [  4175.8481,  16551.9121],\n",
+       "        [  4059.5552,  16346.1387],\n",
+       "        [  3940.4321,  16137.5840],\n",
+       "        [  3818.4849,  15926.2314],\n",
+       "        [  3693.7271,  15712.0645],\n",
+       "        [  3566.1792,  15495.0674],\n",
+       "        [  3435.8423,  15275.2217],\n",
+       "        [  3302.7402,  15052.5166],\n",
+       "        [  3166.8794,  14826.9316],\n",
+       "        [  3028.2856,  14598.4541],\n",
+       "        [  2886.9744,  14367.0732],\n",
+       "        [  2742.9578,  14132.7754],\n",
+       "        [  2596.2632,  13895.5527],\n",
+       "        [  2446.9082,  13655.3965],\n",
+       "        [  2294.9160,  13412.2988],\n",
+       "        [  2140.3057,  13166.2578],\n",
+       "        [  1983.1049,  12917.2715],\n",
+       "        [  1823.3358,  12665.3389],\n",
+       "        [  1661.0265,  12410.4668],\n",
+       "        [  1496.2025,  12152.6582],\n",
+       "        [  1328.8881,  11891.9248],\n",
+       "        [  1159.1161,  11628.2754],\n",
+       "        [   986.9128,  11361.7295],\n",
+       "        [   812.3110,  11092.3027],\n",
+       "        [   635.3432,  10820.0215],\n",
+       "        [   456.0395,  10544.9111],\n",
+       "        [   274.4296,  10266.9980],\n",
+       "        [    90.5536,   9986.3164],\n",
+       "        [   -95.5557,   9702.9062],\n",
+       "        [  -283.8658,   9416.8066],\n",
+       "        [  -474.3362,   9128.0625],\n",
+       "        [  -666.9283,   8836.7256],\n",
+       "        [  -861.6072,   8542.8457],\n",
+       "        [ -1058.3326,   8246.4795],\n",
+       "        [ -1257.0614,   7947.6885],\n",
+       "        [ -1457.7579,   7646.5366],\n",
+       "        [ -1660.3741,   7343.0938],\n",
+       "        [ -1864.8766,   7037.4331],\n",
+       "        [ -2071.2124,   6729.6284],\n",
+       "        [ -2279.3440,   6419.7573],\n",
+       "        [ -2489.2266,   6107.9053],\n",
+       "        [ -2700.8127,   5794.1592],\n",
+       "        [ -2914.0540,   5478.6064],\n",
+       "        [ -3128.9082,   5161.3408],\n",
+       "        [ -3345.3254,   4842.4609],\n",
+       "        [ -3563.2578,   4522.0620],\n",
+       "        [ -3782.6621,   4200.2432],\n",
+       "        [ -4003.4785,   3877.1113],\n",
+       "        [ -4225.6655,   3552.7739],\n",
+       "        [ -4449.1670,   3227.3389],\n",
+       "        [ -4673.9390,   2900.9102],\n",
+       "        [ -4899.9214,   2573.6091],\n",
+       "        [ -5127.0708,   2245.5396],\n",
+       "        [ -5355.3281,   1916.8188],\n",
+       "        [ -5584.6387,   1587.5674],\n",
+       "        [ -5814.9585,   1257.8988],\n",
+       "        [ -6046.2251,    927.9265],\n",
+       "        [ -6278.3887,    597.7721],\n",
+       "        [ -6511.3916,    267.5494],\n",
+       "        [ -6745.1802,    -62.6202],\n",
+       "        [ -6979.6953,   -392.6248],\n",
+       "        [ -7214.8867,   -722.3431],\n",
+       "        [ -7450.6968,  -1051.6656],\n",
+       "        [ -7687.0645,  -1380.4707],\n",
+       "        [ -7923.9414,  -1708.6517],\n",
+       "        [ -8161.2617,  -2036.0929],\n",
+       "        [ -8398.9756,  -2362.6887],\n",
+       "        [ -8637.0215,  -2688.3247],\n",
+       "        [ -8875.3379,  -3012.9009],\n",
+       "        [ -9113.8770,  -3336.3120],\n",
+       "        [ -9352.5791,  -3658.4565],\n",
+       "        [ -9591.3760,  -3979.2349],\n",
+       "        [ -9830.2197,  -4298.5537],\n",
+       "        [-10069.0479,  -4616.3193],\n",
+       "        [-10307.8008,  -4932.4395],\n",
+       "        [-10546.4258,  -5246.8350],\n",
+       "        [-10784.8613,  -5559.4189],\n",
+       "        [-11023.0479,  -5870.1079],\n",
+       "        [-11260.9297,  -6178.8330],\n",
+       "        [-11498.4473,  -7402.2427],\n",
+       "        [-11735.5410,  -7673.4224],\n",
+       "        [-11972.1562,  -7938.4004]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/gpytorch/kernels/rbf_kernel_grad.py b/gpytorch/kernels/rbf_kernel_grad.py
index 595ab7f98..d6a39e0df 100644
--- a/gpytorch/kernels/rbf_kernel_grad.py
+++ b/gpytorch/kernels/rbf_kernel_grad.py
@@ -21,7 +21,7 @@ def __init__(
         # TODO: Add support for ARD
         if ard_num_dims is not None:
             raise RuntimeError('ARD is not supported with derivative observations yet!')
-            
+
         super(RBFKernelGrad, self).__init__(
             ard_num_dims=None,
             batch_size=1,
@@ -36,7 +36,7 @@ def __init__(
     def forward(self, x1, x2, diag=False, **params):
         _, n, d = x1.size()
         if not diag:
-            K = torch.zeros(n*(d+1), n*(d+1))
+            K = torch.zeros(*x1.shape[:-2], n * (d + 1), n * (d + 1))
             x1_ = x1.div(self.lengthscale)
             x2_ = x2.div(self.lengthscale)
             x1_, x2_ = self._create_input_grid(x1_, x2_, **params)
@@ -46,21 +46,20 @@ def forward(self, x1, x2, diag=False, **params):
 
             # 1) Kernel block
             K_11 = diff.pow(2).div(-2).exp()
-            K[:n, :n] = K_11
+            K[..., :n, :n] = K_11
 
             shape_list = [1] * len(K_11.shape[:-2])
             left_shape_list = shape_list + [d, 1]
             right_shape_list = shape_list + [1, d]
 
             # 2) Gradient block
-            print(K_11.shape)
             K_rep = K_11.unsqueeze(-1).repeat(*(shape_list + [1, 1, d]))
             all_K = (all_diff / self.lengthscale) * K_rep
             deriv_K = all_K.transpose(-3, -1).transpose(-2, -1).contiguous().view(d * n, n)
 
-            K[:n, n:] = deriv_K.t()
-            K[n:, :n] = deriv_K
-            
+            K[..., :n, n:] = deriv_K.t()
+            K[..., n:, :n] = deriv_K
+
             # 3) Hessian block
 
             outer_prod = all_diff.transpose(-3, -1).transpose(-2, -1).contiguous().view(d * n, n)
@@ -71,10 +70,11 @@ def forward(self, x1, x2, diag=False, **params):
             diag_part = DiagLazyTensor(NonLazyTensor(repeated_K).diag()).evaluate()
             diag_part = diag_part / self.lengthscale.pow(2)
 
-            K[n:, n:] = diag_part - (outer_prod / self.lengthscale.pow(2)) * repeated_K
+            K[..., n:, n:] = diag_part - (outer_prod / self.lengthscale.pow(2)) * repeated_K
 
             pi = torch.arange(n * (d + 1)).view(d + 1, n).t().contiguous().view((n * (d + 1)))
-            K = K[pi, :][:, pi]
+            K = K[..., pi, :][..., :, pi]
+            print(K.shape)
 
             return K
         else:
@@ -84,16 +84,15 @@ def forward(self, x1, x2, diag=False, **params):
 
             k_diag = torch.cat((kernel_diag, grad_diags), dim=-1)
             pi = torch.arange(n * (d + 1)).view(d + 1, n).t().contiguous().view((n * (d + 1)))
-            return k_diag[pi]
-
+            return k_diag[..., pi]
 
     def size(self, x1, x2):
         """
         Given `n` data points in `d` dimensions, RBFKernelGrad returns an `n(d+1) x n(d+1)` kernel
         matrix.
         """
-        non_batch_size = ((x1.size(-1)+1) * x1.size(-2), (x2.size(-1)+1) * x2.size(-2))
+        non_batch_size = ((x1.size(-1) + 1) * x1.size(-2), (x2.size(-1) + 1) * x2.size(-2))
         if x1.ndimension() == 3:
             return torch.Size((x1.size(0),) + non_batch_size)
         else:
-            return torch.Size(non_batch_size)
\ No newline at end of file
+            return torch.Size(non_batch_size)

From 7d54fcfc8ff83e24ec496986d8178d35643583f3 Mon Sep 17 00:00:00 2001
From: dme65 <dme65@cornell.edu>
Date: Thu, 3 Jan 2019 23:29:24 -0500
Subject: [PATCH 03/25] Working gpderiv in 1d

---
 examples/Simple_GP_Derivative_Example.ipynb | 360 ++++----------------
 gpytorch/kernels/rbf_kernel_grad.py         |  63 ++--
 2 files changed, 96 insertions(+), 327 deletions(-)

diff --git a/examples/Simple_GP_Derivative_Example.ipynb b/examples/Simple_GP_Derivative_Example.ipynb
index a38bcfbb7..8ad9540e9 100644
--- a/examples/Simple_GP_Derivative_Example.ipynb
+++ b/examples/Simple_GP_Derivative_Example.ipynb
@@ -2,14 +2,24 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "import gpytorch\n",
     "import math\n",
     "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "%matplotlib inline\n",
     "%load_ext autoreload\n",
@@ -18,21 +28,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "train_x = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
     "\n",
+    "# train_y = torch.stack([\n",
+    "#     torch.sin(train_x * (10 * math.pi)),\n",
+    "#     10 * math.pi * torch.cos(10 * math.pi * train_x),\n",
+    "# ], -1).squeeze(1)\n",
     "train_y = torch.stack([\n",
-    "    torch.sin(train_x * (2 * math.pi)),\n",
-    "    torch.cos(train_x * (2 * math.pi)),\n",
+    "    (6*train_x - 2).pow(2) * torch.sin(12*train_x - 4),\n",
+    "    12*(6*train_x - 2) * torch.sin(12*train_x - 4) + 12*(6*train_x - 2).pow(2) * torch.cos(12*train_x - 4),\n",
     "], -1).squeeze(1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,119 +62,43 @@
     "        return gpytorch.distributions.MultitaskMultivariateNormal(mean_x, covar_x)\n",
     "\n",
     "    \n",
-    "likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihoodKronecker(num_tasks=train_x.size(-1))\n",
+    "likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihood(num_tasks=2)\n",
     "model = GPModelWithDerivatives(train_x, train_y, likelihood)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([1, 200, 200])\n",
-      "Iter 1/50 - Loss: 111.286\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 2/50 - Loss: 125.321\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 3/50 - Loss: 258.181\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 4/50 - Loss: 174.509\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 5/50 - Loss: 480.031\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 6/50 - Loss: 205.248\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 7/50 - Loss: 881.470\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 8/50 - Loss: 1699.690\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 9/50 - Loss: 1286.258\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 10/50 - Loss: 5270.967\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 11/50 - Loss: 9693.424\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 12/50 - Loss: 6059.958\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 13/50 - Loss: -5199.472\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 14/50 - Loss: -170.532\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 15/50 - Loss: -6329.444\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 16/50 - Loss: -135065.984\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 17/50 - Loss: 206.190\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 18/50 - Loss: 239.457\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 19/50 - Loss: 205.388\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 20/50 - Loss: 140.232\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 21/50 - Loss: 132.896\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 22/50 - Loss: 141.811\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 23/50 - Loss: 137.427\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 24/50 - Loss: 138.412\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 25/50 - Loss: 138.847\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 26/50 - Loss: 143.090\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 27/50 - Loss: 142.166\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 28/50 - Loss: 135.707\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 29/50 - Loss: 122.478\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 30/50 - Loss: 136.192\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 31/50 - Loss: 133.821\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 32/50 - Loss: 138.390\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 33/50 - Loss: 55.152\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 34/50 - Loss: -366.331\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 35/50 - Loss: -8708.603\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 36/50 - Loss: 346347.875\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 37/50 - Loss: 143.309\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 38/50 - Loss: 110.308\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 39/50 - Loss: 61.255\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 40/50 - Loss: 145.943\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 41/50 - Loss: 140.922\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 42/50 - Loss: 133.338\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 43/50 - Loss: 146.797\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 44/50 - Loss: 178.403\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 45/50 - Loss: 200.601\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 46/50 - Loss: 261.491\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 47/50 - Loss: 476.087\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 48/50 - Loss: 1569.892\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 49/50 - Loss: 39871.793\n",
-      "torch.Size([1, 200, 200])\n",
-      "Iter 50/50 - Loss: -8705.504\n"
+     "ename": "AttributeError",
+     "evalue": "'RBFKernelGrad' object has no attribute '_create_input_grid'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    283\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    285\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'RBFKernelGrad' object has no attribute '_create_input_grid'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-3cdeb8c67688>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mmll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Iter %d/%d - Loss: %.3f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/mlls/exact_marginal_log_likelihood.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, output, target, *params)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;31m# Get the log prob of the marginal distribution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlikelihood\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_prob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0;31m# Add terms for SGPR / when inducing points are learned\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/distributions/multitask_multivariate_normal.py\u001b[0m in \u001b[0;36mlog_prob\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mlog_prob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMultitaskMultivariateNormal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_prob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/distributions/multivariate_normal.py\u001b[0m in \u001b[0;36mlog_prob\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;31m# Get log determininat and first part of quadratic form\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m         \u001b[0minv_quad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_det\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcovar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv_quad_log_det\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minv_quad_rhs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_det\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.5\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minv_quad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_det\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiff\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/lazy/lazy_tensor.py\u001b[0m in \u001b[0;36minv_quad_log_det\u001b[0;34m(self, inv_quad_rhs, log_det, reduce_inv_quad)\u001b[0m\n\u001b[1;32m    742\u001b[0m                 )\n\u001b[1;32m    743\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 744\u001b[0;31m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepresentation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    745\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minv_quad_rhs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    746\u001b[0m             \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0minv_quad_rhs\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/lazy/lazy_tensor.py\u001b[0m in \u001b[0;36mrepresentation\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    969\u001b[0m                 \u001b[0mrepresentation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLazyTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 971\u001b[0;31m                 \u001b[0mrepresentation\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepresentation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    972\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Representation of a LazyTensor should consist only of Tensors\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/lazy/lazy_tensor.py\u001b[0m in \u001b[0;36mrepresentation\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    969\u001b[0m                 \u001b[0mrepresentation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLazyTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 971\u001b[0;31m                 \u001b[0mrepresentation\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepresentation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    972\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Representation of a LazyTensor should consist only of Tensors\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/lazy/lazy_evaluated_kernel_tensor.py\u001b[0m in \u001b[0;36mrepresentation\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrepresentation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate_kernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepresentation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrepresentation_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/lazy/lazy_evaluated_kernel_tensor.py\u001b[0m in \u001b[0;36mevaluate_kernel\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    197\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0msettings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlazily_evaluate_kernels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                 self._cached_kernel_eval = self.kernel(\n\u001b[0;32m--> 199\u001b[0;31m                     \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiag\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_dims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m                 )\n\u001b[1;32m    201\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze_row\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/kernels/kernel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x1, x2, diag, batch_dims, **params)\u001b[0m\n\u001b[1;32m    377\u001b[0m                 \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLazyEvaluatedKernelTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_dims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 379\u001b[0;31m                 \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKernel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_dims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    380\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m             \u001b[0;31m# Now we'll make sure that the shape we're getting makes sense\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/kernels/rbf_kernel_grad.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x1, x2, diag, **params)\u001b[0m\n\u001b[1;32m     49\u001b[0m             \u001b[0mx1_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlengthscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m             \u001b[0mx2_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx2\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlengthscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0mx1_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_input_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0mall_diff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx1_\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mx2_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    284\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    271\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 273\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    274\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_deprecation\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLOG_DEPRECATION_MSG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMODULES_WITH_LOG_PARAMS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    533\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m         raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n\u001b[0;32m--> 535\u001b[0;31m             type(self).__name__, name))\n\u001b[0m\u001b[1;32m    536\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'RBFKernelGrad' object has no attribute '_create_input_grid'"
      ]
     }
    ],
@@ -188,66 +126,20 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 5,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([1, 200, 200])\n"
-     ]
-    }
-   ],
    "source": [
-    "covar = gpytorch.kernels.RBFKernelGrad()\n",
-    "K = covar(train_x).evaluate_kernel()"
+    "## Predicting on 200 test points: Gradient looks weird for last 100 points"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.0000,  0.0000,  0.9999,  ...,  0.7431,  0.3532,  0.7352],\n",
-       "        [ 0.0000,  2.0814, -0.0210,  ...,  1.0721, -0.7352,  1.0606],\n",
-       "        [ 0.9999, -0.0210,  1.0000,  ...,  0.7509,  0.3607,  0.7431],\n",
-       "        ...,\n",
-       "        [ 0.7431, -1.0721,  0.7509,  ...,  2.0814, -0.0210,  0.0303],\n",
-       "        [ 0.3532, -0.7352,  0.3607,  ..., -0.0210,  1.0000,  0.0000],\n",
-       "        [ 0.7352, -1.0606,  0.7431,  ..., -0.0303,  0.0000,  2.0814]],\n",
-       "       grad_fn=<SelectBackward>)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "K.evaluate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([1, 200, 200])\n",
-      "torch.Size([1, 200, 200])\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x216 with 2 Axes>"
       ]
@@ -268,23 +160,18 @@
     "\n",
     "# Make predictions\n",
     "with torch.no_grad(), gpytorch.fast_pred_var():\n",
-    "    test_x = train_x\n",
+    "    test_x = torch.linspace(0, 1, 201)\n",
     "    predictions = likelihood(model(test_x))\n",
     "    mean = predictions.mean\n",
-    "    lower, upper = predictions.confidence_region()\n",
+    "#     lower, upper = predictions.confidence_region()\n",
     "    \n",
-    "# This contains predictions for both tasks, flattened out\n",
-    "# The first half of the predictions is for the first task\n",
-    "# The second half is for the second task\n",
-    "\n",
     "# Plot training data as black stars\n",
     "y1_ax.plot(train_x.detach().numpy(), train_y[:, 0].detach().numpy(), 'k*')\n",
     "# Predictive mean as blue line\n",
     "y1_ax.plot(test_x.numpy(), mean[:, 0].numpy(), 'b')\n",
     "# Shade in confidence \n",
-    "y1_ax.fill_between(test_x.squeeze().numpy(), lower[:, 0].numpy(), upper[:, 0].numpy(), alpha=0.5)\n",
-    "y1_ax.set_ylim([-3, 3])\n",
-    "y1_ax.legend(['Observed Data', 'Mean', 'Confidence'])\n",
+    "# y1_ax.fill_between(test_x.numpy(), lower[:, 0].numpy(), upper[:, 0].numpy(), alpha=0.5)\n",
+    "y1_ax.legend(['Observed Values', 'Mean', 'Confidence'])\n",
     "y1_ax.set_title('Observed Values (Likelihood)')\n",
     "\n",
     "# Plot training data as black stars\n",
@@ -292,139 +179,12 @@
     "# Predictive mean as blue line\n",
     "y2_ax.plot(test_x.numpy(), mean[:, 1].numpy(), 'b')\n",
     "# Shade in confidence \n",
-    "y2_ax.fill_between(test_x.squeeze().numpy(), lower[:, 1].numpy(), upper[:, 1].numpy(), alpha=0.5)\n",
-    "y2_ax.set_ylim([-3, 3])\n",
-    "y2_ax.legend(['Observed Data', 'Mean', 'Confidence'])\n",
-    "y2_ax.set_title('Observed Values (Likelihood)')\n",
+    "# y2_ax.fill_between(test_x.numpy(), lower[:, 1].numpy(), upper[:, 1].numpy(), alpha=0.5)\n",
+    "y2_ax.legend(['Observed Derivatives', 'Mean', 'Confidence'])\n",
+    "y2_ax.set_title('Observed Derivatives (Likelihood)')\n",
     "\n",
     "None"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  5778.8516,  19794.4297],\n",
-       "        [  5714.1924,  19636.9375],\n",
-       "        [  5646.6792,  19476.7695],\n",
-       "        [  5576.3120,  19313.9375],\n",
-       "        [  5503.0776,  19148.4453],\n",
-       "        [  5426.9751,  18980.2988],\n",
-       "        [  5347.9956,  18809.5039],\n",
-       "        [  5266.1445,  18636.0625],\n",
-       "        [  5181.4106,  18459.9727],\n",
-       "        [  5093.7974,  18281.2305],\n",
-       "        [  5003.2998,  18099.8320],\n",
-       "        [  4909.9219,  17915.7695],\n",
-       "        [  4813.6606,  17729.0391],\n",
-       "        [  4714.5283,  17539.6270],\n",
-       "        [  4612.5186,  17347.5215],\n",
-       "        [  4507.6392,  17152.7129],\n",
-       "        [  4399.8940,  16955.1836],\n",
-       "        [  4289.2969,  16754.9219],\n",
-       "        [  4175.8481,  16551.9121],\n",
-       "        [  4059.5552,  16346.1387],\n",
-       "        [  3940.4321,  16137.5840],\n",
-       "        [  3818.4849,  15926.2314],\n",
-       "        [  3693.7271,  15712.0645],\n",
-       "        [  3566.1792,  15495.0674],\n",
-       "        [  3435.8423,  15275.2217],\n",
-       "        [  3302.7402,  15052.5166],\n",
-       "        [  3166.8794,  14826.9316],\n",
-       "        [  3028.2856,  14598.4541],\n",
-       "        [  2886.9744,  14367.0732],\n",
-       "        [  2742.9578,  14132.7754],\n",
-       "        [  2596.2632,  13895.5527],\n",
-       "        [  2446.9082,  13655.3965],\n",
-       "        [  2294.9160,  13412.2988],\n",
-       "        [  2140.3057,  13166.2578],\n",
-       "        [  1983.1049,  12917.2715],\n",
-       "        [  1823.3358,  12665.3389],\n",
-       "        [  1661.0265,  12410.4668],\n",
-       "        [  1496.2025,  12152.6582],\n",
-       "        [  1328.8881,  11891.9248],\n",
-       "        [  1159.1161,  11628.2754],\n",
-       "        [   986.9128,  11361.7295],\n",
-       "        [   812.3110,  11092.3027],\n",
-       "        [   635.3432,  10820.0215],\n",
-       "        [   456.0395,  10544.9111],\n",
-       "        [   274.4296,  10266.9980],\n",
-       "        [    90.5536,   9986.3164],\n",
-       "        [   -95.5557,   9702.9062],\n",
-       "        [  -283.8658,   9416.8066],\n",
-       "        [  -474.3362,   9128.0625],\n",
-       "        [  -666.9283,   8836.7256],\n",
-       "        [  -861.6072,   8542.8457],\n",
-       "        [ -1058.3326,   8246.4795],\n",
-       "        [ -1257.0614,   7947.6885],\n",
-       "        [ -1457.7579,   7646.5366],\n",
-       "        [ -1660.3741,   7343.0938],\n",
-       "        [ -1864.8766,   7037.4331],\n",
-       "        [ -2071.2124,   6729.6284],\n",
-       "        [ -2279.3440,   6419.7573],\n",
-       "        [ -2489.2266,   6107.9053],\n",
-       "        [ -2700.8127,   5794.1592],\n",
-       "        [ -2914.0540,   5478.6064],\n",
-       "        [ -3128.9082,   5161.3408],\n",
-       "        [ -3345.3254,   4842.4609],\n",
-       "        [ -3563.2578,   4522.0620],\n",
-       "        [ -3782.6621,   4200.2432],\n",
-       "        [ -4003.4785,   3877.1113],\n",
-       "        [ -4225.6655,   3552.7739],\n",
-       "        [ -4449.1670,   3227.3389],\n",
-       "        [ -4673.9390,   2900.9102],\n",
-       "        [ -4899.9214,   2573.6091],\n",
-       "        [ -5127.0708,   2245.5396],\n",
-       "        [ -5355.3281,   1916.8188],\n",
-       "        [ -5584.6387,   1587.5674],\n",
-       "        [ -5814.9585,   1257.8988],\n",
-       "        [ -6046.2251,    927.9265],\n",
-       "        [ -6278.3887,    597.7721],\n",
-       "        [ -6511.3916,    267.5494],\n",
-       "        [ -6745.1802,    -62.6202],\n",
-       "        [ -6979.6953,   -392.6248],\n",
-       "        [ -7214.8867,   -722.3431],\n",
-       "        [ -7450.6968,  -1051.6656],\n",
-       "        [ -7687.0645,  -1380.4707],\n",
-       "        [ -7923.9414,  -1708.6517],\n",
-       "        [ -8161.2617,  -2036.0929],\n",
-       "        [ -8398.9756,  -2362.6887],\n",
-       "        [ -8637.0215,  -2688.3247],\n",
-       "        [ -8875.3379,  -3012.9009],\n",
-       "        [ -9113.8770,  -3336.3120],\n",
-       "        [ -9352.5791,  -3658.4565],\n",
-       "        [ -9591.3760,  -3979.2349],\n",
-       "        [ -9830.2197,  -4298.5537],\n",
-       "        [-10069.0479,  -4616.3193],\n",
-       "        [-10307.8008,  -4932.4395],\n",
-       "        [-10546.4258,  -5246.8350],\n",
-       "        [-10784.8613,  -5559.4189],\n",
-       "        [-11023.0479,  -5870.1079],\n",
-       "        [-11260.9297,  -6178.8330],\n",
-       "        [-11498.4473,  -7402.2427],\n",
-       "        [-11735.5410,  -7673.4224],\n",
-       "        [-11972.1562,  -7938.4004]])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/gpytorch/kernels/rbf_kernel_grad.py b/gpytorch/kernels/rbf_kernel_grad.py
index d6a39e0df..56432b1b1 100644
--- a/gpytorch/kernels/rbf_kernel_grad.py
+++ b/gpytorch/kernels/rbf_kernel_grad.py
@@ -34,56 +34,65 @@ def __init__(
         )
 
     def forward(self, x1, x2, diag=False, **params):
-        _, n, d = x1.size()
+        if len(x1.size()) == 1:
+            d = 1
+            n1, n2 = x1.size()[0], x2.size()[0]
+        elif len(x1.size()) == 2:
+            n1, d = x1.size()
+            n2, _ = x2.size()
+        else:
+            _, n1, d = x1.size()
+            _, n2, _ = x2.size()
+
         if not diag:
-            K = torch.zeros(*x1.shape[:-2], n * (d + 1), n * (d + 1))
-            x1_ = x1.div(self.lengthscale)
-            x2_ = x2.div(self.lengthscale)
+            K = torch.zeros(*x1.shape[:-2], n1 * (d + 1), n2 * (d + 1))
+            x1_ = x1 / self.lengthscale
+            x2_ = x2 / self.lengthscale
             x1_, x2_ = self._create_input_grid(x1_, x2_, **params)
 
             all_diff = (x1_ - x2_)
             diff = all_diff.norm(2, dim=-1)
+            all_diff = all_diff.squeeze(len(all_diff.size()) - 1)
 
             # 1) Kernel block
             K_11 = diff.pow(2).div(-2).exp()
-            K[..., :n, :n] = K_11
+            K[..., :n1, :n2] = K_11
 
             shape_list = [1] * len(K_11.shape[:-2])
-            left_shape_list = shape_list + [d, 1]
-            right_shape_list = shape_list + [1, d]
 
-            # 2) Gradient block
-            K_rep = K_11.unsqueeze(-1).repeat(*(shape_list + [1, 1, d]))
+            # 2) First gradient block
+            K_rep = K_11.repeat(*(shape_list + [1, d]))
             all_K = (all_diff / self.lengthscale) * K_rep
-            deriv_K = all_K.transpose(-3, -1).transpose(-2, -1).contiguous().view(d * n, n)
-
-            K[..., :n, n:] = deriv_K.t()
-            K[..., n:, :n] = deriv_K
-
-            # 3) Hessian block
+            deriv_K = all_K.contiguous().view(d * n1, n2)
+            K[..., :n1, n2:] = deriv_K
 
-            outer_prod = all_diff.transpose(-3, -1).transpose(-2, -1).contiguous().view(d * n, n)
-            outer_prod = outer_prod.repeat(*right_shape_list)
-
-            repeated_K = K_11.repeat(*left_shape_list).repeat(*right_shape_list)
-
-            diag_part = DiagLazyTensor(NonLazyTensor(repeated_K).diag()).evaluate()
-            diag_part = diag_part / self.lengthscale.pow(2)
+            # 3) Second gradient block
+            K_rep = K_11.repeat(*(shape_list + [d, 1]))
+            all_K = (all_diff / self.lengthscale) * K_rep
+            deriv_K = all_K.permute(0, 2, 1).contiguous().view(d * n2, n1)
+            K[..., n1:, :n2] = -deriv_K.transpose(0, 1)
 
-            K[..., n:, n:] = diag_part - (outer_prod / self.lengthscale.pow(2)) * repeated_K
+            # 4) Hessian block
+            outer_prod = all_diff.contiguous().view(d * n1, n2)
+            outer_prod = - (outer_prod * outer_prod) / self.lengthscale.pow(2)
+            I = torch.eye(d) / self.lengthscale.pow(2)
+            outer_prod += I.repeat(shape_list + [n1, n2])
+            repeated_K = K_11.repeat(shape_list + [d, d])
+            K[..., n1:, n2:] = outer_prod * repeated_K
 
-            pi = torch.arange(n * (d + 1)).view(d + 1, n).t().contiguous().view((n * (d + 1)))
-            K = K[..., pi, :][..., :, pi]
-            print(K.shape)
+            pi1 = torch.arange(n1 * (d + 1)).view(d + 1, n1).t().contiguous().view((n1 * (d + 1)))
+            pi2 = torch.arange(n2 * (d + 1)).view(d + 1, n2).t().contiguous().view((n2 * (d + 1)))
+            K = K[..., pi1, :][..., :, pi2]
 
             return K
         else:
             kernel_diag = super(RBFKernelGrad, self).forward(x1, x2, diag=True)
+            print(kernel_diag.size())
             # TODO: This will change when ARD is supported
             grad_diags = (1 / self.lengthscale.squeeze().pow(2)).expand_as(kernel_diag).repeat(1, d)
 
             k_diag = torch.cat((kernel_diag, grad_diags), dim=-1)
-            pi = torch.arange(n * (d + 1)).view(d + 1, n).t().contiguous().view((n * (d + 1)))
+            pi = torch.arange(n1 * (d + 1)).view(d + 1, n2).t().contiguous().view((n1 * (d + 1)))
             return k_diag[..., pi]
 
     def size(self, x1, x2):

From 6b5188a155e2a8e69db3a2e84f0ffe9d90786161 Mon Sep 17 00:00:00 2001
From: dme65 <dme65@cornell.edu>
Date: Fri, 4 Jan 2019 00:08:39 -0500
Subject: [PATCH 04/25] Temporarily adding _create_input_grid + fixing diagonal

---
 examples/Simple_GP_Derivative_Example.ipynb | 122 ++++++++++++--------
 gpytorch/kernels/rbf_kernel_grad.py         |  44 +++++--
 2 files changed, 113 insertions(+), 53 deletions(-)

diff --git a/examples/Simple_GP_Derivative_Example.ipynb b/examples/Simple_GP_Derivative_Example.ipynb
index 8ad9540e9..37cdd4492 100644
--- a/examples/Simple_GP_Derivative_Example.ipynb
+++ b/examples/Simple_GP_Derivative_Example.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -28,25 +28,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_x = torch.linspace(0, 1, 100).unsqueeze(-1)\n",
+    "lb, ub = 0.0, 5*math.pi\n",
     "\n",
-    "# train_y = torch.stack([\n",
-    "#     torch.sin(train_x * (10 * math.pi)),\n",
-    "#     10 * math.pi * torch.cos(10 * math.pi * train_x),\n",
-    "# ], -1).squeeze(1)\n",
+    "train_x = torch.linspace(lb, ub, 100).unsqueeze(-1)\n",
     "train_y = torch.stack([\n",
-    "    (6*train_x - 2).pow(2) * torch.sin(12*train_x - 4),\n",
-    "    12*(6*train_x - 2) * torch.sin(12*train_x - 4) + 12*(6*train_x - 2).pow(2) * torch.cos(12*train_x - 4),\n",
+    "    torch.sin(2*train_x) + torch.cos(train_x), \n",
+    "    -torch.sin(train_x) + 2*torch.cos(2*train_x)\n",
     "], -1).squeeze(1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,37 +65,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "'RBFKernelGrad' object has no attribute '_create_input_grid'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    283\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    285\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'RBFKernelGrad' object has no attribute '_create_input_grid'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-8-3cdeb8c67688>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mmll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Iter %d/%d - Loss: %.3f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/mlls/exact_marginal_log_likelihood.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, output, target, *params)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;31m# Get the log prob of the marginal distribution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlikelihood\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_prob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0;31m# Add terms for SGPR / when inducing points are learned\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/distributions/multitask_multivariate_normal.py\u001b[0m in \u001b[0;36mlog_prob\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mlog_prob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMultitaskMultivariateNormal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_prob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/distributions/multivariate_normal.py\u001b[0m in \u001b[0;36mlog_prob\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;31m# Get log determininat and first part of quadratic form\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m         \u001b[0minv_quad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_det\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcovar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv_quad_log_det\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minv_quad_rhs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_det\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.5\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minv_quad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_det\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiff\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/lazy/lazy_tensor.py\u001b[0m in \u001b[0;36minv_quad_log_det\u001b[0;34m(self, inv_quad_rhs, log_det, reduce_inv_quad)\u001b[0m\n\u001b[1;32m    742\u001b[0m                 )\n\u001b[1;32m    743\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 744\u001b[0;31m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepresentation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    745\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minv_quad_rhs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    746\u001b[0m             \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0minv_quad_rhs\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/lazy/lazy_tensor.py\u001b[0m in \u001b[0;36mrepresentation\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    969\u001b[0m                 \u001b[0mrepresentation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLazyTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 971\u001b[0;31m                 \u001b[0mrepresentation\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepresentation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    972\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Representation of a LazyTensor should consist only of Tensors\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/lazy/lazy_tensor.py\u001b[0m in \u001b[0;36mrepresentation\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    969\u001b[0m                 \u001b[0mrepresentation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLazyTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 971\u001b[0;31m                 \u001b[0mrepresentation\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepresentation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    972\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Representation of a LazyTensor should consist only of Tensors\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/lazy/lazy_evaluated_kernel_tensor.py\u001b[0m in \u001b[0;36mrepresentation\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrepresentation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate_kernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepresentation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrepresentation_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/lazy/lazy_evaluated_kernel_tensor.py\u001b[0m in \u001b[0;36mevaluate_kernel\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    197\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0msettings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlazily_evaluate_kernels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                 self._cached_kernel_eval = self.kernel(\n\u001b[0;32m--> 199\u001b[0;31m                     \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiag\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_dims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m                 )\n\u001b[1;32m    201\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze_row\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/kernels/kernel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x1, x2, diag, batch_dims, **params)\u001b[0m\n\u001b[1;32m    377\u001b[0m                 \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLazyEvaluatedKernelTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_dims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 379\u001b[0;31m                 \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKernel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_dims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    380\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m             \u001b[0;31m# Now we'll make sure that the shape we're getting makes sense\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/kernels/rbf_kernel_grad.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x1, x2, diag, **params)\u001b[0m\n\u001b[1;32m     49\u001b[0m             \u001b[0mx1_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlengthscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m             \u001b[0mx2_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx2\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlengthscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0mx1_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_input_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0mall_diff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx1_\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mx2_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    284\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/GitHub/gpytorch/gpytorch/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    271\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 273\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    274\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_deprecation\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLOG_DEPRECATION_MSG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMODULES_WITH_LOG_PARAMS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    533\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m         raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n\u001b[0;32m--> 535\u001b[0;31m             type(self).__name__, name))\n\u001b[0m\u001b[1;32m    536\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'RBFKernelGrad' object has no attribute '_create_input_grid'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iter 1/50 - Loss: 128.564\n",
+      "Iter 2/50 - Loss: 123.239\n",
+      "Iter 3/50 - Loss: 119.471\n",
+      "Iter 4/50 - Loss: 114.999\n",
+      "Iter 5/50 - Loss: 111.768\n",
+      "Iter 6/50 - Loss: 108.339\n",
+      "Iter 7/50 - Loss: 105.248\n",
+      "Iter 8/50 - Loss: 100.716\n",
+      "Iter 9/50 - Loss: 97.057\n",
+      "Iter 10/50 - Loss: 94.148\n",
+      "Iter 11/50 - Loss: 91.176\n",
+      "Iter 12/50 - Loss: 86.638\n",
+      "Iter 13/50 - Loss: 80.966\n",
+      "Iter 14/50 - Loss: 77.911\n",
+      "Iter 15/50 - Loss: 72.931\n",
+      "Iter 16/50 - Loss: 70.648\n",
+      "Iter 17/50 - Loss: 66.616\n",
+      "Iter 18/50 - Loss: 63.672\n",
+      "Iter 19/50 - Loss: 56.317\n",
+      "Iter 20/50 - Loss: 49.783\n",
+      "Iter 21/50 - Loss: 48.089\n",
+      "Iter 22/50 - Loss: 47.358\n",
+      "Iter 23/50 - Loss: 41.717\n",
+      "Iter 24/50 - Loss: 33.350\n",
+      "Iter 25/50 - Loss: 29.555\n",
+      "Iter 26/50 - Loss: 24.426\n",
+      "Iter 27/50 - Loss: 25.261\n",
+      "Iter 28/50 - Loss: 18.025\n",
+      "Iter 29/50 - Loss: 13.513\n",
+      "Iter 30/50 - Loss: 9.686\n",
+      "Iter 31/50 - Loss: 4.416\n",
+      "Iter 32/50 - Loss: -0.345\n",
+      "Iter 33/50 - Loss: -5.136\n",
+      "Iter 34/50 - Loss: -12.027\n",
+      "Iter 35/50 - Loss: -16.741\n",
+      "Iter 36/50 - Loss: -20.538\n",
+      "Iter 37/50 - Loss: -27.107\n",
+      "Iter 38/50 - Loss: -28.344\n",
+      "Iter 39/50 - Loss: -33.494\n",
+      "Iter 40/50 - Loss: -39.287\n",
+      "Iter 41/50 - Loss: -42.385\n",
+      "Iter 42/50 - Loss: -49.378\n",
+      "Iter 43/50 - Loss: -54.289\n",
+      "Iter 44/50 - Loss: -58.479\n",
+      "Iter 45/50 - Loss: -61.146\n",
+      "Iter 46/50 - Loss: -66.331\n",
+      "Iter 47/50 - Loss: -72.558\n",
+      "Iter 48/50 - Loss: -78.223\n",
+      "Iter 49/50 - Loss: -79.689\n",
+      "Iter 50/50 - Loss: -78.371\n"
      ]
     }
    ],
@@ -134,14 +157,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 576x216 with 2 Axes>"
+       "<Figure size 864x432 with 2 Axes>"
       ]
      },
      "metadata": {
@@ -156,21 +179,21 @@
     "likelihood.eval()\n",
     "\n",
     "# Initialize plots\n",
-    "f, (y1_ax, y2_ax) = plt.subplots(1, 2, figsize=(8, 3))\n",
+    "f, (y1_ax, y2_ax) = plt.subplots(1, 2, figsize=(12, 6))\n",
     "\n",
     "# Make predictions\n",
     "with torch.no_grad(), gpytorch.fast_pred_var():\n",
-    "    test_x = torch.linspace(0, 1, 201)\n",
+    "    test_x = torch.linspace(lb, ub, 10000)\n",
     "    predictions = likelihood(model(test_x))\n",
     "    mean = predictions.mean\n",
-    "#     lower, upper = predictions.confidence_region()\n",
+    "    lower, upper = predictions.confidence_region()\n",
     "    \n",
     "# Plot training data as black stars\n",
     "y1_ax.plot(train_x.detach().numpy(), train_y[:, 0].detach().numpy(), 'k*')\n",
     "# Predictive mean as blue line\n",
     "y1_ax.plot(test_x.numpy(), mean[:, 0].numpy(), 'b')\n",
     "# Shade in confidence \n",
-    "# y1_ax.fill_between(test_x.numpy(), lower[:, 0].numpy(), upper[:, 0].numpy(), alpha=0.5)\n",
+    "y1_ax.fill_between(test_x.numpy(), lower[:, 0].numpy(), upper[:, 0].numpy(), alpha=0.5)\n",
     "y1_ax.legend(['Observed Values', 'Mean', 'Confidence'])\n",
     "y1_ax.set_title('Observed Values (Likelihood)')\n",
     "\n",
@@ -179,12 +202,19 @@
     "# Predictive mean as blue line\n",
     "y2_ax.plot(test_x.numpy(), mean[:, 1].numpy(), 'b')\n",
     "# Shade in confidence \n",
-    "# y2_ax.fill_between(test_x.numpy(), lower[:, 1].numpy(), upper[:, 1].numpy(), alpha=0.5)\n",
+    "y2_ax.fill_between(test_x.numpy(), lower[:, 1].numpy(), upper[:, 1].numpy(), alpha=0.5)\n",
     "y2_ax.legend(['Observed Derivatives', 'Mean', 'Confidence'])\n",
     "y2_ax.set_title('Observed Derivatives (Likelihood)')\n",
     "\n",
     "None"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/gpytorch/kernels/rbf_kernel_grad.py b/gpytorch/kernels/rbf_kernel_grad.py
index 56432b1b1..5f529c0d3 100644
--- a/gpytorch/kernels/rbf_kernel_grad.py
+++ b/gpytorch/kernels/rbf_kernel_grad.py
@@ -85,16 +85,46 @@ def forward(self, x1, x2, diag=False, **params):
             K = K[..., pi1, :][..., :, pi2]
 
             return K
-        else:
+        else: # TODO: This will change when ARD is supported
             kernel_diag = super(RBFKernelGrad, self).forward(x1, x2, diag=True)
-            print(kernel_diag.size())
-            # TODO: This will change when ARD is supported
-            grad_diags = (1 / self.lengthscale.squeeze().pow(2)).expand_as(kernel_diag).repeat(1, d)
-
-            k_diag = torch.cat((kernel_diag, grad_diags), dim=-1)
-            pi = torch.arange(n1 * (d + 1)).view(d + 1, n2).t().contiguous().view((n1 * (d + 1)))
+            grad_diag = (1 / self.lengthscale.item() ** 2) * torch.ones(1, n2*d)
+            k_diag = torch.cat((kernel_diag, grad_diag), dim=-1)
+            pi = torch.arange(n2 * (d + 1)).view(d + 1, n2).t().contiguous().view((n2 * (d + 1)))
             return k_diag[..., pi]
 
+    def _create_input_grid(self, x1, x2, diag=False, batch_dims=None, **params):  # TODO: Remove me please
+        """
+        This is a helper method for creating a grid of the kernel's inputs.
+        Use this helper rather than maually creating a meshgrid.
+        The grid dimensions depend on the kernel's evaluation mode.
+        Args:
+            :attr:`x1` (Tensor `n x d` or `b x n x d`)
+            :attr:`x2` (Tensor `m x d` or `b x m x d`) - for diag mode, these must be the same inputs
+        Returns:
+            (:class:`Tensor`, :class:`Tensor) corresponding to the gridded `x1` and `x2`.
+            The shape depends on the kernel's mode
+            * `full_covar`: (`b x n x 1 x d` and `b x 1 x m x d`)
+            * `full_covar` with `batch_dims=(0, 2)`: (`b x k x n x 1 x 1` and `b x k x 1 x m x 1`)
+            * `diag`: (`b x n x d` and `b x n x d`)
+            * `diag` with `batch_dims=(0, 2)`: (`b x k x n x 1` and `b x k x n x 1`)
+        """
+        x1_, x2_ = x1, x2
+        if batch_dims == (0, 2):
+            x1_ = x1_.view(*x1.size()[:-1], -1, 1)
+            x1_ = x1_.permute(0, -2, *list(range(1, x1_.dim() - 2)), -1).contiguous()
+            x1_ = x1_.view(-1, *x1_.size()[2:])
+            if torch.equal(x1, x2):
+                x2_ = x1_
+            else:
+                x2_ = x2_.view(*x2.size()[:-1], -1, 1)
+                x2_ = x2_.permute(0, -2, *list(range(1, x2_.dim() - 2)), -1).contiguous()
+                x2_ = x2_.view(-1, *x2_.size()[2:])
+
+        if diag:
+            return x1_, x2_
+        else:
+            return x1_.unsqueeze(-2), x2_.unsqueeze(-3)
+
     def size(self, x1, x2):
         """
         Given `n` data points in `d` dimensions, RBFKernelGrad returns an `n(d+1) x n(d+1)` kernel

From 6b02be6521f5773a460393116828670d5e9997eb Mon Sep 17 00:00:00 2001
From: dme65 <dme65@cornell.edu>
Date: Sun, 6 Jan 2019 20:17:42 -0500
Subject: [PATCH 05/25] rbf_kernel_grad works in more than 1d + example

---
 .../Simple_GP_Derivative_Example-2d.ipynb     | 267 ++++++++++++++++++
 examples/Simple_GP_Derivative_Example.ipynb   | 208 ++++++++------
 gpytorch/kernels/rbf_kernel_grad.py           |  89 ++----
 gpytorch/kernels/scale_kernel.py              |   5 +-
 gpytorch/means/constant_mean_grad.py          |   6 +-
 5 files changed, 427 insertions(+), 148 deletions(-)
 create mode 100644 examples/Simple_GP_Derivative_Example-2d.ipynb

diff --git a/examples/Simple_GP_Derivative_Example-2d.ipynb b/examples/Simple_GP_Derivative_Example-2d.ipynb
new file mode 100644
index 000000000..d678d0536
--- /dev/null
+++ b/examples/Simple_GP_Derivative_Example-2d.ipynb
@@ -0,0 +1,267 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import gpytorch\n",
+    "import math\n",
+    "from matplotlib import cm\n",
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def franke(X, Y):\n",
+    "    term1 = .75*torch.exp(-((9*X - 2).pow(2) + (9*Y - 2).pow(2)) / 4)\n",
+    "    term2 = .75*torch.exp(-((9*X + 1).pow(2))/49 - (9*Y + 1) / 10)\n",
+    "    term3 = .5*torch.exp(-((9*X - 7).pow(2) + (9*Y - 3).pow(2)) / 4)\n",
+    "    term4 = .2*torch.exp(-(9*X - 4).pow(2) - (9*Y - 7).pow(2))\n",
+    "    \n",
+    "    f = term1 + term2 + term3 - term4\n",
+    "    dfx = -2*(9*X - 2)*9/4 * term1 - 2*(9*X + 1)*9/49 * term2 + \\\n",
+    "          -2*(9*X - 7)*9/4 * term3 + 2*(9*X - 4)*9 * term4\n",
+    "    dfy = -2*(9*Y - 2)*9/4 * term1 - 9/10 * term2 + \\\n",
+    "          -2*(9*Y - 3)*9/4 * term3 + 2*(9*Y - 7)*9 * term4\n",
+    "    \n",
+    "    return f, dfx, dfy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xv, yv = torch.meshgrid([torch.linspace(0, 1, 10), torch.linspace(0, 1, 10)])\n",
+    "train_x = torch.cat((\n",
+    "    xv.contiguous().view(xv.numel(), 1), \n",
+    "    yv.contiguous().view(yv.numel(), 1)),\n",
+    "    dim=1\n",
+    ")\n",
+    "\n",
+    "f, dfx, dfy = franke(train_x[:, 0], train_x[:, 1])\n",
+    "train_y = torch.stack([f, dfx, dfy], -1).squeeze(1)\n",
+    "train_y += 0.01 * torch.randn(train_y.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "class GPModelWithDerivatives(gpytorch.models.ExactGP):\n",
+    "    def __init__(self, train_x, train_y, likelihood):\n",
+    "        super(GPModelWithDerivatives, self).__init__(train_x, train_y, likelihood)\n",
+    "        self.mean_module = gpytorch.means.ConstantMeanGrad()\n",
+    "        self.base_kernel = gpytorch.kernels.RBFKernelGrad()\n",
+    "        self.covar_module = gpytorch.kernels.ScaleKernel(self.base_kernel)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        mean_x = self.mean_module(x)\n",
+    "        covar_x = self.covar_module(x)\n",
+    "        return gpytorch.distributions.MultitaskMultivariateNormal(mean_x, covar_x)\n",
+    "\n",
+    "likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihood(num_tasks=3)\n",
+    "likelihood.initialize(noise=0.01)\n",
+    "likelihood.raw_noise.requires_grad = False\n",
+    "model = GPModelWithDerivatives(train_x, train_y, likelihood)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iter 1/50 - Loss: 121.066   log_lengthscale: -0.367   log_noise: -4.605\n",
+      "Iter 2/50 - Loss: 117.639   log_lengthscale: -0.439   log_noise: -4.605\n",
+      "Iter 3/50 - Loss: 113.527   log_lengthscale: -0.514   log_noise: -4.605\n",
+      "Iter 4/50 - Loss: 109.725   log_lengthscale: -0.590   log_noise: -4.605\n",
+      "Iter 5/50 - Loss: 105.410   log_lengthscale: -0.668   log_noise: -4.605\n",
+      "Iter 6/50 - Loss: 101.371   log_lengthscale: -0.748   log_noise: -4.605\n",
+      "Iter 7/50 - Loss: 96.672   log_lengthscale: -0.829   log_noise: -4.605\n",
+      "Iter 8/50 - Loss: 92.044   log_lengthscale: -0.911   log_noise: -4.605\n",
+      "Iter 9/50 - Loss: 87.955   log_lengthscale: -0.994   log_noise: -4.605\n",
+      "Iter 10/50 - Loss: 83.548   log_lengthscale: -1.078   log_noise: -4.605\n",
+      "Iter 11/50 - Loss: 79.874   log_lengthscale: -1.154   log_noise: -4.605\n",
+      "Iter 12/50 - Loss: 76.233   log_lengthscale: -1.213   log_noise: -4.605\n",
+      "Iter 13/50 - Loss: 73.201   log_lengthscale: -1.253   log_noise: -4.605\n",
+      "Iter 14/50 - Loss: 71.075   log_lengthscale: -1.269   log_noise: -4.605\n",
+      "Iter 15/50 - Loss: 67.782   log_lengthscale: -1.265   log_noise: -4.605\n",
+      "Iter 16/50 - Loss: 62.595   log_lengthscale: -1.248   log_noise: -4.605\n",
+      "Iter 17/50 - Loss: 58.523   log_lengthscale: -1.221   log_noise: -4.605\n",
+      "Iter 18/50 - Loss: 53.579   log_lengthscale: -1.184   log_noise: -4.605\n",
+      "Iter 19/50 - Loss: 48.988   log_lengthscale: -1.155   log_noise: -4.605\n",
+      "Iter 20/50 - Loss: 45.275   log_lengthscale: -1.136   log_noise: -4.605\n",
+      "Iter 21/50 - Loss: 43.737   log_lengthscale: -1.134   log_noise: -4.605\n",
+      "Iter 22/50 - Loss: 39.329   log_lengthscale: -1.145   log_noise: -4.605\n",
+      "Iter 23/50 - Loss: 36.874   log_lengthscale: -1.167   log_noise: -4.605\n",
+      "Iter 24/50 - Loss: 32.077   log_lengthscale: -1.198   log_noise: -4.605\n",
+      "Iter 25/50 - Loss: 28.373   log_lengthscale: -1.238   log_noise: -4.605\n",
+      "Iter 26/50 - Loss: 25.229   log_lengthscale: -1.280   log_noise: -4.605\n",
+      "Iter 27/50 - Loss: 21.574   log_lengthscale: -1.322   log_noise: -4.605\n",
+      "Iter 28/50 - Loss: 18.326   log_lengthscale: -1.359   log_noise: -4.605\n",
+      "Iter 29/50 - Loss: 12.031   log_lengthscale: -1.387   log_noise: -4.605\n",
+      "Iter 30/50 - Loss: 10.735   log_lengthscale: -1.405   log_noise: -4.605\n",
+      "Iter 31/50 - Loss: 7.898   log_lengthscale: -1.411   log_noise: -4.605\n",
+      "Iter 32/50 - Loss: 5.609   log_lengthscale: -1.404   log_noise: -4.605\n",
+      "Iter 33/50 - Loss: -1.033   log_lengthscale: -1.392   log_noise: -4.605\n",
+      "Iter 34/50 - Loss: -4.513   log_lengthscale: -1.383   log_noise: -4.605\n",
+      "Iter 35/50 - Loss: -5.047   log_lengthscale: -1.378   log_noise: -4.605\n",
+      "Iter 36/50 - Loss: -9.458   log_lengthscale: -1.381   log_noise: -4.605\n",
+      "Iter 37/50 - Loss: -10.794   log_lengthscale: -1.396   log_noise: -4.605\n",
+      "Iter 38/50 - Loss: -17.460   log_lengthscale: -1.420   log_noise: -4.605\n",
+      "Iter 39/50 - Loss: -18.758   log_lengthscale: -1.451   log_noise: -4.605\n",
+      "Iter 40/50 - Loss: -21.076   log_lengthscale: -1.485   log_noise: -4.605\n",
+      "Iter 41/50 - Loss: -19.847   log_lengthscale: -1.514   log_noise: -4.605\n",
+      "Iter 42/50 - Loss: -24.356   log_lengthscale: -1.531   log_noise: -4.605\n",
+      "Iter 43/50 - Loss: -25.890   log_lengthscale: -1.536   log_noise: -4.605\n",
+      "Iter 44/50 - Loss: -27.752   log_lengthscale: -1.538   log_noise: -4.605\n",
+      "Iter 45/50 - Loss: -31.736   log_lengthscale: -1.538   log_noise: -4.605\n",
+      "Iter 46/50 - Loss: -34.399   log_lengthscale: -1.541   log_noise: -4.605\n",
+      "Iter 47/50 - Loss: -35.859   log_lengthscale: -1.549   log_noise: -4.605\n",
+      "Iter 48/50 - Loss: -37.764   log_lengthscale: -1.562   log_noise: -4.605\n",
+      "Iter 49/50 - Loss: -39.020   log_lengthscale: -1.577   log_noise: -4.605\n",
+      "Iter 50/50 - Loss: -40.391   log_lengthscale: -1.593   log_noise: -4.605\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Find optimal model hyperparameters\n",
+    "model.train()\n",
+    "likelihood.train()\n",
+    "\n",
+    "# Use the adam optimizer\n",
+    "optimizer = torch.optim.Adam([\n",
+    "    {'params': model.parameters()},  # Includes GaussianLikelihood parameters\n",
+    "], lr=0.1)\n",
+    "\n",
+    "# \"Loss\" for GPs - the marginal log likelihood\n",
+    "mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)\n",
+    "\n",
+    "with gpytorch.settings.max_preconditioner_size(20), gpytorch.settings.max_cg_iterations(50):\n",
+    "    n_iter = 50\n",
+    "    for i in range(n_iter):\n",
+    "        optimizer.zero_grad()\n",
+    "        output = model(train_x)\n",
+    "        loss = -mll(output, train_y)\n",
+    "        loss.backward()\n",
+    "        print('Iter %d/%d - Loss: %.3f   log_lengthscale: %.3f   log_noise: %.3f' % (\n",
+    "            i + 1, n_iter, loss.item(),\n",
+    "            model.covar_module.base_kernel.log_lengthscale.item(),\n",
+    "            model.likelihood.log_noise.item()\n",
+    "        ))\n",
+    "        optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predicting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x720 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set into eval mode\n",
+    "model.eval()\n",
+    "likelihood.eval()\n",
+    "\n",
+    "# Initialize plots\n",
+    "fig, ax = plt.subplots(2, 3, figsize=(14, 10))\n",
+    "\n",
+    "# Test points\n",
+    "n1, n2 = 50, 50\n",
+    "xv, yv = torch.meshgrid([torch.linspace(0, 1, n1), torch.linspace(0, 1, n2)])\n",
+    "f, dfx, dfy = franke(xv, yv)\n",
+    "\n",
+    "# Make predictions\n",
+    "with torch.no_grad(), gpytorch.settings.max_preconditioner_size(20), gpytorch.settings.max_cg_iterations(50):\n",
+    "    test_x = torch.stack([xv.reshape(n1*n2, 1), yv.reshape(n1*n2, 1)], -1).squeeze(1)\n",
+    "    predictions = likelihood(model(test_x))\n",
+    "    mean = predictions.mean\n",
+    "  \n",
+    "extent=(xv.min(), xv.max(), yv.max(), yv.min())\n",
+    "ax[0, 0].imshow(f, extent=extent, cmap=cm.jet)\n",
+    "ax[0, 0].set_title('True values')\n",
+    "ax[0, 1].imshow(dfx, extent=extent, cmap=cm.jet)\n",
+    "ax[0, 1].set_title('True x-derivatives')\n",
+    "ax[0, 2].imshow(dfy, extent=extent, cmap=cm.jet)\n",
+    "ax[0, 2].set_title('True y-derivatives')\n",
+    "\n",
+    "ax[1, 0].imshow(mean[:, 0].detach().numpy().reshape(n1, n2), extent=extent, cmap=cm.jet)\n",
+    "ax[1, 0].set_title('Predicted values')\n",
+    "ax[1, 1].imshow(mean[:, 1].detach().numpy().reshape(n1, n2), extent=extent, cmap=cm.jet)\n",
+    "ax[1, 1].set_title('Predicted x-derivatives')\n",
+    "ax[1, 2].imshow(mean[:, 2].detach().numpy().reshape(n1, n2), extent=extent, cmap=cm.jet)\n",
+    "ax[1, 2].set_title('Predicted y-derivatives')\n",
+    "\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/Simple_GP_Derivative_Example.ipynb b/examples/Simple_GP_Derivative_Example.ipynb
index 37cdd4492..304edc4a3 100644
--- a/examples/Simple_GP_Derivative_Example.ipynb
+++ b/examples/Simple_GP_Derivative_Example.ipynb
@@ -2,18 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torch\n",
     "import gpytorch\n",
@@ -28,22 +19,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "lb, ub = 0.0, 5*math.pi\n",
+    "n = 100\n",
     "\n",
-    "train_x = torch.linspace(lb, ub, 100).unsqueeze(-1)\n",
+    "train_x = torch.linspace(lb, ub, n).unsqueeze(-1) + 0.01*torch.randn(n, 1)\n",
     "train_y = torch.stack([\n",
     "    torch.sin(2*train_x) + torch.cos(train_x), \n",
     "    -torch.sin(train_x) + 2*torch.cos(2*train_x)\n",
-    "], -1).squeeze(1)"
+    "], -1).squeeze(1)\n",
+    "\n",
+    "train_y += 0.05 * torch.randn(n, 2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,77 +45,79 @@
     "    def __init__(self, train_x, train_y, likelihood):\n",
     "        super(GPModelWithDerivatives, self).__init__(train_x, train_y, likelihood)\n",
     "        self.mean_module = gpytorch.means.ConstantMeanGrad()\n",
-    "        self.covar_module = gpytorch.kernels.RBFKernelGrad()\n",
+    "        self.base_kernel = gpytorch.kernels.RBFKernelGrad()\n",
+    "        self.covar_module =gpytorch.kernels.RBFKernelGrad()\n",
     "\n",
     "    def forward(self, x):\n",
     "        mean_x = self.mean_module(x)\n",
     "        covar_x = self.covar_module(x)\n",
     "        return gpytorch.distributions.MultitaskMultivariateNormal(mean_x, covar_x)\n",
     "\n",
-    "    \n",
     "likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihood(num_tasks=2)\n",
+    "likelihood.initialize(noise=0.01)\n",
+    "likelihood.raw_noise.requires_grad = False\n",
     "model = GPModelWithDerivatives(train_x, train_y, likelihood)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iter 1/50 - Loss: 128.564\n",
-      "Iter 2/50 - Loss: 123.239\n",
-      "Iter 3/50 - Loss: 119.471\n",
-      "Iter 4/50 - Loss: 114.999\n",
-      "Iter 5/50 - Loss: 111.768\n",
-      "Iter 6/50 - Loss: 108.339\n",
-      "Iter 7/50 - Loss: 105.248\n",
-      "Iter 8/50 - Loss: 100.716\n",
-      "Iter 9/50 - Loss: 97.057\n",
-      "Iter 10/50 - Loss: 94.148\n",
-      "Iter 11/50 - Loss: 91.176\n",
-      "Iter 12/50 - Loss: 86.638\n",
-      "Iter 13/50 - Loss: 80.966\n",
-      "Iter 14/50 - Loss: 77.911\n",
-      "Iter 15/50 - Loss: 72.931\n",
-      "Iter 16/50 - Loss: 70.648\n",
-      "Iter 17/50 - Loss: 66.616\n",
-      "Iter 18/50 - Loss: 63.672\n",
-      "Iter 19/50 - Loss: 56.317\n",
-      "Iter 20/50 - Loss: 49.783\n",
-      "Iter 21/50 - Loss: 48.089\n",
-      "Iter 22/50 - Loss: 47.358\n",
-      "Iter 23/50 - Loss: 41.717\n",
-      "Iter 24/50 - Loss: 33.350\n",
-      "Iter 25/50 - Loss: 29.555\n",
-      "Iter 26/50 - Loss: 24.426\n",
-      "Iter 27/50 - Loss: 25.261\n",
-      "Iter 28/50 - Loss: 18.025\n",
-      "Iter 29/50 - Loss: 13.513\n",
-      "Iter 30/50 - Loss: 9.686\n",
-      "Iter 31/50 - Loss: 4.416\n",
-      "Iter 32/50 - Loss: -0.345\n",
-      "Iter 33/50 - Loss: -5.136\n",
-      "Iter 34/50 - Loss: -12.027\n",
-      "Iter 35/50 - Loss: -16.741\n",
-      "Iter 36/50 - Loss: -20.538\n",
-      "Iter 37/50 - Loss: -27.107\n",
-      "Iter 38/50 - Loss: -28.344\n",
-      "Iter 39/50 - Loss: -33.494\n",
-      "Iter 40/50 - Loss: -39.287\n",
-      "Iter 41/50 - Loss: -42.385\n",
-      "Iter 42/50 - Loss: -49.378\n",
-      "Iter 43/50 - Loss: -54.289\n",
-      "Iter 44/50 - Loss: -58.479\n",
-      "Iter 45/50 - Loss: -61.146\n",
-      "Iter 46/50 - Loss: -66.331\n",
-      "Iter 47/50 - Loss: -72.558\n",
-      "Iter 48/50 - Loss: -78.223\n",
-      "Iter 49/50 - Loss: -79.689\n",
-      "Iter 50/50 - Loss: -78.371\n"
+      "Iter 1/50 - Loss: 119.235   log_lengthscale: 0.693   log_noise: -4.605\n",
+      "Iter 2/50 - Loss: 113.814   log_lengthscale: 0.744   log_noise: -4.605\n",
+      "Iter 3/50 - Loss: 109.178   log_lengthscale: 0.796   log_noise: -4.605\n",
+      "Iter 4/50 - Loss: 104.745   log_lengthscale: 0.850   log_noise: -4.605\n",
+      "Iter 5/50 - Loss: 100.758   log_lengthscale: 0.903   log_noise: -4.605\n",
+      "Iter 6/50 - Loss: 96.835   log_lengthscale: 0.955   log_noise: -4.605\n",
+      "Iter 7/50 - Loss: 93.260   log_lengthscale: 1.003   log_noise: -4.605\n",
+      "Iter 8/50 - Loss: 89.637   log_lengthscale: 1.039   log_noise: -4.605\n",
+      "Iter 9/50 - Loss: 85.894   log_lengthscale: 1.064   log_noise: -4.605\n",
+      "Iter 10/50 - Loss: 81.914   log_lengthscale: 1.076   log_noise: -4.605\n",
+      "Iter 11/50 - Loss: 77.104   log_lengthscale: 1.075   log_noise: -4.605\n",
+      "Iter 12/50 - Loss: 72.943   log_lengthscale: 1.062   log_noise: -4.605\n",
+      "Iter 13/50 - Loss: 68.337   log_lengthscale: 1.041   log_noise: -4.605\n",
+      "Iter 14/50 - Loss: 63.714   log_lengthscale: 1.015   log_noise: -4.605\n",
+      "Iter 15/50 - Loss: 59.472   log_lengthscale: 0.990   log_noise: -4.605\n",
+      "Iter 16/50 - Loss: 55.383   log_lengthscale: 0.968   log_noise: -4.605\n",
+      "Iter 17/50 - Loss: 51.043   log_lengthscale: 0.950   log_noise: -4.605\n",
+      "Iter 18/50 - Loss: 46.585   log_lengthscale: 0.936   log_noise: -4.605\n",
+      "Iter 19/50 - Loss: 42.139   log_lengthscale: 0.927   log_noise: -4.605\n",
+      "Iter 20/50 - Loss: 38.583   log_lengthscale: 0.923   log_noise: -4.605\n",
+      "Iter 21/50 - Loss: 33.755   log_lengthscale: 0.927   log_noise: -4.605\n",
+      "Iter 22/50 - Loss: 29.036   log_lengthscale: 0.937   log_noise: -4.605\n",
+      "Iter 23/50 - Loss: 25.205   log_lengthscale: 0.950   log_noise: -4.605\n",
+      "Iter 24/50 - Loss: 20.363   log_lengthscale: 0.969   log_noise: -4.605\n",
+      "Iter 25/50 - Loss: 16.542   log_lengthscale: 0.991   log_noise: -4.605\n",
+      "Iter 26/50 - Loss: 11.797   log_lengthscale: 1.013   log_noise: -4.605\n",
+      "Iter 27/50 - Loss: 7.511   log_lengthscale: 1.034   log_noise: -4.605\n",
+      "Iter 28/50 - Loss: 3.347   log_lengthscale: 1.052   log_noise: -4.605\n",
+      "Iter 29/50 - Loss: -0.306   log_lengthscale: 1.068   log_noise: -4.605\n",
+      "Iter 30/50 - Loss: -4.388   log_lengthscale: 1.078   log_noise: -4.605\n",
+      "Iter 31/50 - Loss: -8.212   log_lengthscale: 1.081   log_noise: -4.605\n",
+      "Iter 32/50 - Loss: -12.233   log_lengthscale: 1.078   log_noise: -4.605\n",
+      "Iter 33/50 - Loss: -16.304   log_lengthscale: 1.070   log_noise: -4.605\n",
+      "Iter 34/50 - Loss: -20.214   log_lengthscale: 1.059   log_noise: -4.605\n",
+      "Iter 35/50 - Loss: -23.128   log_lengthscale: 1.043   log_noise: -4.605\n",
+      "Iter 36/50 - Loss: -25.845   log_lengthscale: 1.029   log_noise: -4.605\n",
+      "Iter 37/50 - Loss: -29.738   log_lengthscale: 1.023   log_noise: -4.605\n",
+      "Iter 38/50 - Loss: -33.471   log_lengthscale: 1.020   log_noise: -4.605\n",
+      "Iter 39/50 - Loss: -35.789   log_lengthscale: 1.019   log_noise: -4.605\n",
+      "Iter 40/50 - Loss: -39.070   log_lengthscale: 1.024   log_noise: -4.605\n",
+      "Iter 41/50 - Loss: -41.703   log_lengthscale: 1.032   log_noise: -4.605\n",
+      "Iter 42/50 - Loss: -44.084   log_lengthscale: 1.040   log_noise: -4.605\n",
+      "Iter 43/50 - Loss: -47.036   log_lengthscale: 1.054   log_noise: -4.605\n",
+      "Iter 44/50 - Loss: -49.336   log_lengthscale: 1.067   log_noise: -4.605\n",
+      "Iter 45/50 - Loss: -50.759   log_lengthscale: 1.079   log_noise: -4.605\n",
+      "Iter 46/50 - Loss: -53.638   log_lengthscale: 1.086   log_noise: -4.605\n",
+      "Iter 47/50 - Loss: -55.337   log_lengthscale: 1.089   log_noise: -4.605\n",
+      "Iter 48/50 - Loss: -57.165   log_lengthscale: 1.085   log_noise: -4.605\n",
+      "Iter 49/50 - Loss: -59.122   log_lengthscale: 1.079   log_noise: -4.605\n",
+      "Iter 50/50 - Loss: -60.640   log_lengthscale: 1.070   log_noise: -4.605\n"
      ]
     }
    ],
@@ -138,31 +134,36 @@
     "# \"Loss\" for GPs - the marginal log likelihood\n",
     "mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)\n",
     "\n",
-    "n_iter = 50\n",
-    "for i in range(n_iter):\n",
-    "    optimizer.zero_grad()\n",
-    "    output = model(train_x)\n",
-    "    loss = -mll(output, train_y)\n",
-    "    loss.backward()\n",
-    "    print('Iter %d/%d - Loss: %.3f' % (i + 1, n_iter, loss.item()))\n",
-    "    optimizer.step()"
+    "with gpytorch.settings.max_preconditioner_size(20), gpytorch.settings.max_cg_iterations(50):\n",
+    "    n_iter = 50\n",
+    "    for i in range(n_iter):\n",
+    "        optimizer.zero_grad()\n",
+    "        output = model(train_x)\n",
+    "        loss = -mll(output, train_y)\n",
+    "        loss.backward()\n",
+    "        print('Iter %d/%d - Loss: %.3f   log_lengthscale: %.3f   log_noise: %.3f' % (\n",
+    "            i + 1, n_iter, loss.item(),\n",
+    "            model.covar_module.lengthscale.item(),\n",
+    "            model.likelihood.log_noise.item()\n",
+    "        ))        \n",
+    "        optimizer.step()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Predicting on 200 test points: Gradient looks weird for last 100 points"
+    "## Predicting "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 2 Axes>"
       ]
@@ -175,6 +176,7 @@
    ],
    "source": [
     "# Set into eval mode\n",
+    "model.train()\n",
     "model.eval()\n",
     "likelihood.eval()\n",
     "\n",
@@ -182,8 +184,8 @@
     "f, (y1_ax, y2_ax) = plt.subplots(1, 2, figsize=(12, 6))\n",
     "\n",
     "# Make predictions\n",
-    "with torch.no_grad(), gpytorch.fast_pred_var():\n",
-    "    test_x = torch.linspace(lb, ub, 10000)\n",
+    "with torch.no_grad(), gpytorch.settings.max_preconditioner_size(20), gpytorch.settings.max_cg_iterations(50):\n",
+    "    test_x = torch.linspace(lb, ub, 500)\n",
     "    predictions = likelihood(model(test_x))\n",
     "    mean = predictions.mean\n",
     "    lower, upper = predictions.confidence_region()\n",
@@ -195,7 +197,7 @@
     "# Shade in confidence \n",
     "y1_ax.fill_between(test_x.numpy(), lower[:, 0].numpy(), upper[:, 0].numpy(), alpha=0.5)\n",
     "y1_ax.legend(['Observed Values', 'Mean', 'Confidence'])\n",
-    "y1_ax.set_title('Observed Values (Likelihood)')\n",
+    "y1_ax.set_title('Function values')\n",
     "\n",
     "# Plot training data as black stars\n",
     "y2_ax.plot(train_x.detach().numpy(), train_y[:, 1].detach().numpy(), 'k*')\n",
@@ -204,11 +206,51 @@
     "# Shade in confidence \n",
     "y2_ax.fill_between(test_x.numpy(), lower[:, 1].numpy(), upper[:, 1].numpy(), alpha=0.5)\n",
     "y2_ax.legend(['Observed Derivatives', 'Mean', 'Confidence'])\n",
-    "y2_ax.set_title('Observed Derivatives (Likelihood)')\n",
+    "y2_ax.set_title('Derivatives')\n",
     "\n",
     "None"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0.0100]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.likelihood.noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[tensor([0.0077, 0.0078], grad_fn=<SelectBackward>)]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(model.likelihood.noise_covar.noise)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/gpytorch/kernels/rbf_kernel_grad.py b/gpytorch/kernels/rbf_kernel_grad.py
index 5f529c0d3..79d4addb5 100644
--- a/gpytorch/kernels/rbf_kernel_grad.py
+++ b/gpytorch/kernels/rbf_kernel_grad.py
@@ -4,6 +4,7 @@
 from ..utils.deprecation import _deprecate_kwarg
 from torch.nn.functional import softplus
 import torch
+from ..lazy.kronecker_product_lazy_tensor import KroneckerProductLazyTensor
 
 
 class RBFKernelGrad(RBFKernel):
@@ -22,7 +23,7 @@ def __init__(
         if ard_num_dims is not None:
             raise RuntimeError('ARD is not supported with derivative observations yet!')
 
-        super(RBFKernelGrad, self).__init__(
+        super().__init__(
             ard_num_dims=None,
             batch_size=1,
             active_dims=None,
@@ -34,56 +35,53 @@ def __init__(
         )
 
     def forward(self, x1, x2, diag=False, **params):
-        if len(x1.size()) == 1:
-            d = 1
-            n1, n2 = x1.size()[0], x2.size()[0]
-        elif len(x1.size()) == 2:
+        if len(x1.size()) == 2:
             n1, d = x1.size()
-            n2, _ = x2.size()
+            n2, d = x2.size()
         else:
             _, n1, d = x1.size()
             _, n2, _ = x2.size()
 
         if not diag:
-            K = torch.zeros(*x1.shape[:-2], n1 * (d + 1), n2 * (d + 1))
-            x1_ = x1 / self.lengthscale
-            x2_ = x2 / self.lengthscale
-            x1_, x2_ = self._create_input_grid(x1_, x2_, **params)
-
-            all_diff = (x1_ - x2_)
-            diff = all_diff.norm(2, dim=-1)
-            all_diff = all_diff.squeeze(len(all_diff.size()) - 1)
+            ell = self.lengthscale.double()
+            K = torch.zeros(*x1.shape[:-2], n1 * (d + 1), n2 * (d + 1), dtype=torch.double)
+            x1_ = x1.double() / ell
+            x2_ = x2.double() / ell
 
             # 1) Kernel block
-            K_11 = diff.pow(2).div(-2).exp()
+            diff = self._covar_dist(x1_, x2_, square_dist=True, **params)
+            K_11 = diff.div_(-2).exp_()
             K[..., :n1, :n2] = K_11
 
             shape_list = [1] * len(K_11.shape[:-2])
+            outer = x1_.view(shape_list + [n1, 1, d]) - x2_.view(shape_list + [1, n2, d])
+            outer = torch.transpose(outer, -1, -2).contiguous()
 
             # 2) First gradient block
-            K_rep = K_11.repeat(*(shape_list + [1, d]))
-            all_K = (all_diff / self.lengthscale) * K_rep
-            deriv_K = all_K.contiguous().view(d * n1, n2)
-            K[..., :n1, n2:] = deriv_K
+            outer1 = outer.view(shape_list + [n1, n2*d])
+            K[..., :n1, n2:] = outer1 * K_11.repeat(shape_list + [1, d]) / ell
 
             # 3) Second gradient block
-            K_rep = K_11.repeat(*(shape_list + [d, 1]))
-            all_K = (all_diff / self.lengthscale) * K_rep
-            deriv_K = all_K.permute(0, 2, 1).contiguous().view(d * n2, n1)
-            K[..., n1:, :n2] = -deriv_K.transpose(0, 1)
+            outer2 = outer.transpose(-1, -3).contiguous().view(shape_list + [n2, n1*d])
+            outer2 = outer2.transpose(-1, -2) 
+            K[..., n1:, :n2] = -outer2 * K_11.repeat(shape_list + [d, 1]) / ell
 
             # 4) Hessian block
-            outer_prod = all_diff.contiguous().view(d * n1, n2)
-            outer_prod = - (outer_prod * outer_prod) / self.lengthscale.pow(2)
-            I = torch.eye(d) / self.lengthscale.pow(2)
-            outer_prod += I.repeat(shape_list + [n1, n2])
-            repeated_K = K_11.repeat(shape_list + [d, d])
-            K[..., n1:, n2:] = outer_prod * repeated_K
+            outer3 = outer1.repeat(shape_list + [d, 1]) * outer2.repeat(shape_list + [1, d])
+            kp = KroneckerProductLazyTensor(torch.eye(d, d, dtype=torch.double), \
+                torch.ones(n1, n2, dtype=torch.double))
+            fact1 = kp.evaluate() - outer3
+            K[..., n1:, n2:] = fact1 * K_11.repeat(shape_list + [d, d]) / (ell ** 2)
+
+            if n1 == n2 and torch.eq(x1, x2).all():  # Symmetrize for stability
+                K = 0.5*(K.transpose(-1, -2) + K)
 
             pi1 = torch.arange(n1 * (d + 1)).view(d + 1, n1).t().contiguous().view((n1 * (d + 1)))
             pi2 = torch.arange(n2 * (d + 1)).view(d + 1, n2).t().contiguous().view((n2 * (d + 1)))
             K = K[..., pi1, :][..., :, pi2]
 
+            K = K.float()  # Convert back to float
+
             return K
         else: # TODO: This will change when ARD is supported
             kernel_diag = super(RBFKernelGrad, self).forward(x1, x2, diag=True)
@@ -92,39 +90,6 @@ def forward(self, x1, x2, diag=False, **params):
             pi = torch.arange(n2 * (d + 1)).view(d + 1, n2).t().contiguous().view((n2 * (d + 1)))
             return k_diag[..., pi]
 
-    def _create_input_grid(self, x1, x2, diag=False, batch_dims=None, **params):  # TODO: Remove me please
-        """
-        This is a helper method for creating a grid of the kernel's inputs.
-        Use this helper rather than maually creating a meshgrid.
-        The grid dimensions depend on the kernel's evaluation mode.
-        Args:
-            :attr:`x1` (Tensor `n x d` or `b x n x d`)
-            :attr:`x2` (Tensor `m x d` or `b x m x d`) - for diag mode, these must be the same inputs
-        Returns:
-            (:class:`Tensor`, :class:`Tensor) corresponding to the gridded `x1` and `x2`.
-            The shape depends on the kernel's mode
-            * `full_covar`: (`b x n x 1 x d` and `b x 1 x m x d`)
-            * `full_covar` with `batch_dims=(0, 2)`: (`b x k x n x 1 x 1` and `b x k x 1 x m x 1`)
-            * `diag`: (`b x n x d` and `b x n x d`)
-            * `diag` with `batch_dims=(0, 2)`: (`b x k x n x 1` and `b x k x n x 1`)
-        """
-        x1_, x2_ = x1, x2
-        if batch_dims == (0, 2):
-            x1_ = x1_.view(*x1.size()[:-1], -1, 1)
-            x1_ = x1_.permute(0, -2, *list(range(1, x1_.dim() - 2)), -1).contiguous()
-            x1_ = x1_.view(-1, *x1_.size()[2:])
-            if torch.equal(x1, x2):
-                x2_ = x1_
-            else:
-                x2_ = x2_.view(*x2.size()[:-1], -1, 1)
-                x2_ = x2_.permute(0, -2, *list(range(1, x2_.dim() - 2)), -1).contiguous()
-                x2_ = x2_.view(-1, *x2_.size()[2:])
-
-        if diag:
-            return x1_, x2_
-        else:
-            return x1_.unsqueeze(-2), x2_.unsqueeze(-3)
-
     def size(self, x1, x2):
         """
         Given `n` data points in `d` dimensions, RBFKernelGrad returns an `n(d+1) x n(d+1)` kernel
diff --git a/gpytorch/kernels/scale_kernel.py b/gpytorch/kernels/scale_kernel.py
index 5227a4867..4d7ba1d56 100644
--- a/gpytorch/kernels/scale_kernel.py
+++ b/gpytorch/kernels/scale_kernel.py
@@ -94,11 +94,12 @@ def forward(self, x1, x2, batch_dims=None, diag=False, **params):
             outputscales = outputscales.unsqueeze(1).repeat(1, x1.size(-1)).view(-1)
 
         orig_output = self.base_kernel.forward(x1, x2, diag=diag, batch_dims=batch_dims, **params)
-
         if torch.is_tensor(orig_output):
             outputscales = outputscales.view(-1, *([1] * (orig_output.dim() - 1)))
 
         if diag:
             return delazify(orig_output) * outputscales
-
         return orig_output.mul(outputscales)
+
+    def size(self, x1, x2):
+        return self.base_kernel.size(x1, x2)
\ No newline at end of file
diff --git a/gpytorch/means/constant_mean_grad.py b/gpytorch/means/constant_mean_grad.py
index 0d4c2a9ce..2d134b8f4 100644
--- a/gpytorch/means/constant_mean_grad.py
+++ b/gpytorch/means/constant_mean_grad.py
@@ -13,7 +13,11 @@ def __init__(self, prior=None, batch_size=None):
             self.register_prior("mean_prior", prior, "constant")
 
     def forward(self, input):
-        mean = self.constant.unsqueeze(-1).repeat(input.size(0), input.size(1), input.size(2) + 1)
+        mean = self.constant.squeeze().repeat(input.size(-2), input.size(-1) + 1)
+        if input.ndimension() == 3:
+            mean = self.constant.squeeze().repeat(input.size(0), input.size(1), input.size(2) + 1)
+        else:
+            mean = self.constant.squeeze().repeat(input.size(0), input.size(1) + 1)
         mean[..., :, 1:] = 0
         return mean
     

From 212e1f2023382e3c3431d41b7919b4e7af3e30cf Mon Sep 17 00:00:00 2001
From: dme65 <dme65@cornell.edu>
Date: Mon, 7 Jan 2019 16:14:03 -0500
Subject: [PATCH 06/25] Reordering operations to get rid of cancellation

---
 .../Simple_GP_Derivative_Example-2d.ipynb     | 106 ++++++------
 examples/Simple_GP_Derivative_Example.ipynb   | 155 ++++++------------
 gpytorch/kernels/rbf_kernel_grad.py           |  21 +--
 3 files changed, 116 insertions(+), 166 deletions(-)

diff --git a/examples/Simple_GP_Derivative_Example-2d.ipynb b/examples/Simple_GP_Derivative_Example-2d.ipynb
index d678d0536..a66b3f58a 100644
--- a/examples/Simple_GP_Derivative_Example-2d.ipynb
+++ b/examples/Simple_GP_Derivative_Example-2d.ipynb
@@ -78,7 +78,7 @@
     "        return gpytorch.distributions.MultitaskMultivariateNormal(mean_x, covar_x)\n",
     "\n",
     "likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihood(num_tasks=3)\n",
-    "likelihood.initialize(noise=0.01)\n",
+    "likelihood.initialize(noise=0.01*train_y.std())  # Require no less than 1% noise (approximately)\n",
     "likelihood.raw_noise.requires_grad = False\n",
     "model = GPModelWithDerivatives(train_x, train_y, likelihood)"
    ]
@@ -92,56 +92,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iter 1/50 - Loss: 121.066   log_lengthscale: -0.367   log_noise: -4.605\n",
-      "Iter 2/50 - Loss: 117.639   log_lengthscale: -0.439   log_noise: -4.605\n",
-      "Iter 3/50 - Loss: 113.527   log_lengthscale: -0.514   log_noise: -4.605\n",
-      "Iter 4/50 - Loss: 109.725   log_lengthscale: -0.590   log_noise: -4.605\n",
-      "Iter 5/50 - Loss: 105.410   log_lengthscale: -0.668   log_noise: -4.605\n",
-      "Iter 6/50 - Loss: 101.371   log_lengthscale: -0.748   log_noise: -4.605\n",
-      "Iter 7/50 - Loss: 96.672   log_lengthscale: -0.829   log_noise: -4.605\n",
-      "Iter 8/50 - Loss: 92.044   log_lengthscale: -0.911   log_noise: -4.605\n",
-      "Iter 9/50 - Loss: 87.955   log_lengthscale: -0.994   log_noise: -4.605\n",
-      "Iter 10/50 - Loss: 83.548   log_lengthscale: -1.078   log_noise: -4.605\n",
-      "Iter 11/50 - Loss: 79.874   log_lengthscale: -1.154   log_noise: -4.605\n",
-      "Iter 12/50 - Loss: 76.233   log_lengthscale: -1.213   log_noise: -4.605\n",
-      "Iter 13/50 - Loss: 73.201   log_lengthscale: -1.253   log_noise: -4.605\n",
-      "Iter 14/50 - Loss: 71.075   log_lengthscale: -1.269   log_noise: -4.605\n",
-      "Iter 15/50 - Loss: 67.782   log_lengthscale: -1.265   log_noise: -4.605\n",
-      "Iter 16/50 - Loss: 62.595   log_lengthscale: -1.248   log_noise: -4.605\n",
-      "Iter 17/50 - Loss: 58.523   log_lengthscale: -1.221   log_noise: -4.605\n",
-      "Iter 18/50 - Loss: 53.579   log_lengthscale: -1.184   log_noise: -4.605\n",
-      "Iter 19/50 - Loss: 48.988   log_lengthscale: -1.155   log_noise: -4.605\n",
-      "Iter 20/50 - Loss: 45.275   log_lengthscale: -1.136   log_noise: -4.605\n",
-      "Iter 21/50 - Loss: 43.737   log_lengthscale: -1.134   log_noise: -4.605\n",
-      "Iter 22/50 - Loss: 39.329   log_lengthscale: -1.145   log_noise: -4.605\n",
-      "Iter 23/50 - Loss: 36.874   log_lengthscale: -1.167   log_noise: -4.605\n",
-      "Iter 24/50 - Loss: 32.077   log_lengthscale: -1.198   log_noise: -4.605\n",
-      "Iter 25/50 - Loss: 28.373   log_lengthscale: -1.238   log_noise: -4.605\n",
-      "Iter 26/50 - Loss: 25.229   log_lengthscale: -1.280   log_noise: -4.605\n",
-      "Iter 27/50 - Loss: 21.574   log_lengthscale: -1.322   log_noise: -4.605\n",
-      "Iter 28/50 - Loss: 18.326   log_lengthscale: -1.359   log_noise: -4.605\n",
-      "Iter 29/50 - Loss: 12.031   log_lengthscale: -1.387   log_noise: -4.605\n",
-      "Iter 30/50 - Loss: 10.735   log_lengthscale: -1.405   log_noise: -4.605\n",
-      "Iter 31/50 - Loss: 7.898   log_lengthscale: -1.411   log_noise: -4.605\n",
-      "Iter 32/50 - Loss: 5.609   log_lengthscale: -1.404   log_noise: -4.605\n",
-      "Iter 33/50 - Loss: -1.033   log_lengthscale: -1.392   log_noise: -4.605\n",
-      "Iter 34/50 - Loss: -4.513   log_lengthscale: -1.383   log_noise: -4.605\n",
-      "Iter 35/50 - Loss: -5.047   log_lengthscale: -1.378   log_noise: -4.605\n",
-      "Iter 36/50 - Loss: -9.458   log_lengthscale: -1.381   log_noise: -4.605\n",
-      "Iter 37/50 - Loss: -10.794   log_lengthscale: -1.396   log_noise: -4.605\n",
-      "Iter 38/50 - Loss: -17.460   log_lengthscale: -1.420   log_noise: -4.605\n",
-      "Iter 39/50 - Loss: -18.758   log_lengthscale: -1.451   log_noise: -4.605\n",
-      "Iter 40/50 - Loss: -21.076   log_lengthscale: -1.485   log_noise: -4.605\n",
-      "Iter 41/50 - Loss: -19.847   log_lengthscale: -1.514   log_noise: -4.605\n",
-      "Iter 42/50 - Loss: -24.356   log_lengthscale: -1.531   log_noise: -4.605\n",
-      "Iter 43/50 - Loss: -25.890   log_lengthscale: -1.536   log_noise: -4.605\n",
-      "Iter 44/50 - Loss: -27.752   log_lengthscale: -1.538   log_noise: -4.605\n",
-      "Iter 45/50 - Loss: -31.736   log_lengthscale: -1.538   log_noise: -4.605\n",
-      "Iter 46/50 - Loss: -34.399   log_lengthscale: -1.541   log_noise: -4.605\n",
-      "Iter 47/50 - Loss: -35.859   log_lengthscale: -1.549   log_noise: -4.605\n",
-      "Iter 48/50 - Loss: -37.764   log_lengthscale: -1.562   log_noise: -4.605\n",
-      "Iter 49/50 - Loss: -39.020   log_lengthscale: -1.577   log_noise: -4.605\n",
-      "Iter 50/50 - Loss: -40.391   log_lengthscale: -1.593   log_noise: -4.605\n"
+      "Iter 1/50 - Loss: 121.481   log_lengthscale: -0.367   log_noise: -4.714\n",
+      "Iter 2/50 - Loss: 117.704   log_lengthscale: -0.439   log_noise: -4.714\n",
+      "Iter 3/50 - Loss: 114.238   log_lengthscale: -0.514   log_noise: -4.714\n",
+      "Iter 4/50 - Loss: 109.993   log_lengthscale: -0.590   log_noise: -4.714\n",
+      "Iter 5/50 - Loss: 106.198   log_lengthscale: -0.668   log_noise: -4.714\n",
+      "Iter 6/50 - Loss: 101.540   log_lengthscale: -0.746   log_noise: -4.714\n",
+      "Iter 7/50 - Loss: 97.518   log_lengthscale: -0.826   log_noise: -4.714\n",
+      "Iter 8/50 - Loss: 92.896   log_lengthscale: -0.907   log_noise: -4.714\n",
+      "Iter 9/50 - Loss: 88.487   log_lengthscale: -0.989   log_noise: -4.714\n",
+      "Iter 10/50 - Loss: 82.971   log_lengthscale: -1.072   log_noise: -4.714\n",
+      "Iter 11/50 - Loss: 79.673   log_lengthscale: -1.154   log_noise: -4.714\n",
+      "Iter 12/50 - Loss: 77.952   log_lengthscale: -1.219   log_noise: -4.714\n",
+      "Iter 13/50 - Loss: 77.443   log_lengthscale: -1.264   log_noise: -4.714\n",
+      "Iter 14/50 - Loss: 71.981   log_lengthscale: -1.279   log_noise: -4.714\n",
+      "Iter 15/50 - Loss: 67.413   log_lengthscale: -1.275   log_noise: -4.714\n",
+      "Iter 16/50 - Loss: 62.697   log_lengthscale: -1.256   log_noise: -4.714\n",
+      "Iter 17/50 - Loss: 57.064   log_lengthscale: -1.230   log_noise: -4.714\n",
+      "Iter 18/50 - Loss: 53.132   log_lengthscale: -1.203   log_noise: -4.714\n",
+      "Iter 19/50 - Loss: 49.381   log_lengthscale: -1.179   log_noise: -4.714\n",
+      "Iter 20/50 - Loss: 44.819   log_lengthscale: -1.161   log_noise: -4.714\n",
+      "Iter 21/50 - Loss: 43.359   log_lengthscale: -1.157   log_noise: -4.714\n",
+      "Iter 22/50 - Loss: 38.582   log_lengthscale: -1.166   log_noise: -4.714\n",
+      "Iter 23/50 - Loss: 36.792   log_lengthscale: -1.188   log_noise: -4.714\n",
+      "Iter 24/50 - Loss: 29.682   log_lengthscale: -1.222   log_noise: -4.714\n",
+      "Iter 25/50 - Loss: 27.097   log_lengthscale: -1.262   log_noise: -4.714\n",
+      "Iter 26/50 - Loss: 22.476   log_lengthscale: -1.306   log_noise: -4.714\n",
+      "Iter 27/50 - Loss: 19.876   log_lengthscale: -1.349   log_noise: -4.714\n",
+      "Iter 28/50 - Loss: 17.554   log_lengthscale: -1.384   log_noise: -4.714\n",
+      "Iter 29/50 - Loss: 15.018   log_lengthscale: -1.405   log_noise: -4.714\n",
+      "Iter 30/50 - Loss: 9.520   log_lengthscale: -1.411   log_noise: -4.714\n",
+      "Iter 31/50 - Loss: 8.101   log_lengthscale: -1.406   log_noise: -4.714\n",
+      "Iter 32/50 - Loss: 2.132   log_lengthscale: -1.395   log_noise: -4.714\n",
+      "Iter 33/50 - Loss: 0.007   log_lengthscale: -1.384   log_noise: -4.714\n",
+      "Iter 34/50 - Loss: -3.187   log_lengthscale: -1.378   log_noise: -4.714\n",
+      "Iter 35/50 - Loss: -7.931   log_lengthscale: -1.382   log_noise: -4.714\n",
+      "Iter 36/50 - Loss: -9.148   log_lengthscale: -1.401   log_noise: -4.714\n",
+      "Iter 37/50 - Loss: -12.666   log_lengthscale: -1.428   log_noise: -4.714\n",
+      "Iter 38/50 - Loss: -14.253   log_lengthscale: -1.459   log_noise: -4.714\n",
+      "Iter 39/50 - Loss: -17.827   log_lengthscale: -1.487   log_noise: -4.714\n",
+      "Iter 40/50 - Loss: -22.998   log_lengthscale: -1.508   log_noise: -4.714\n",
+      "Iter 41/50 - Loss: -26.465   log_lengthscale: -1.521   log_noise: -4.714\n",
+      "Iter 42/50 - Loss: -25.948   log_lengthscale: -1.531   log_noise: -4.714\n",
+      "Iter 43/50 - Loss: -29.126   log_lengthscale: -1.534   log_noise: -4.714\n",
+      "Iter 44/50 - Loss: -33.415   log_lengthscale: -1.535   log_noise: -4.714\n",
+      "Iter 45/50 - Loss: -34.796   log_lengthscale: -1.539   log_noise: -4.714\n",
+      "Iter 46/50 - Loss: -32.216   log_lengthscale: -1.545   log_noise: -4.714\n",
+      "Iter 47/50 - Loss: -34.801   log_lengthscale: -1.554   log_noise: -4.714\n",
+      "Iter 48/50 - Loss: -37.049   log_lengthscale: -1.565   log_noise: -4.714\n",
+      "Iter 49/50 - Loss: -38.725   log_lengthscale: -1.581   log_noise: -4.714\n",
+      "Iter 50/50 - Loss: -44.302   log_lengthscale: -1.599   log_noise: -4.714\n"
      ]
     }
    ],
@@ -182,12 +182,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1008x720 with 6 Axes>"
       ]
diff --git a/examples/Simple_GP_Derivative_Example.ipynb b/examples/Simple_GP_Derivative_Example.ipynb
index 304edc4a3..e3c403e5d 100644
--- a/examples/Simple_GP_Derivative_Example.ipynb
+++ b/examples/Simple_GP_Derivative_Example.ipynb
@@ -46,7 +46,7 @@
     "        super(GPModelWithDerivatives, self).__init__(train_x, train_y, likelihood)\n",
     "        self.mean_module = gpytorch.means.ConstantMeanGrad()\n",
     "        self.base_kernel = gpytorch.kernels.RBFKernelGrad()\n",
-    "        self.covar_module =gpytorch.kernels.RBFKernelGrad()\n",
+    "        self.covar_module = gpytorch.kernels.ScaleKernel(self.base_kernel)\n",
     "\n",
     "    def forward(self, x):\n",
     "        mean_x = self.mean_module(x)\n",
@@ -54,7 +54,7 @@
     "        return gpytorch.distributions.MultitaskMultivariateNormal(mean_x, covar_x)\n",
     "\n",
     "likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihood(num_tasks=2)\n",
-    "likelihood.initialize(noise=0.01)\n",
+    "likelihood.initialize(noise=0.01*train_y.std())\n",
     "likelihood.raw_noise.requires_grad = False\n",
     "model = GPModelWithDerivatives(train_x, train_y, likelihood)"
    ]
@@ -68,56 +68,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iter 1/50 - Loss: 119.235   log_lengthscale: 0.693   log_noise: -4.605\n",
-      "Iter 2/50 - Loss: 113.814   log_lengthscale: 0.744   log_noise: -4.605\n",
-      "Iter 3/50 - Loss: 109.178   log_lengthscale: 0.796   log_noise: -4.605\n",
-      "Iter 4/50 - Loss: 104.745   log_lengthscale: 0.850   log_noise: -4.605\n",
-      "Iter 5/50 - Loss: 100.758   log_lengthscale: 0.903   log_noise: -4.605\n",
-      "Iter 6/50 - Loss: 96.835   log_lengthscale: 0.955   log_noise: -4.605\n",
-      "Iter 7/50 - Loss: 93.260   log_lengthscale: 1.003   log_noise: -4.605\n",
-      "Iter 8/50 - Loss: 89.637   log_lengthscale: 1.039   log_noise: -4.605\n",
-      "Iter 9/50 - Loss: 85.894   log_lengthscale: 1.064   log_noise: -4.605\n",
-      "Iter 10/50 - Loss: 81.914   log_lengthscale: 1.076   log_noise: -4.605\n",
-      "Iter 11/50 - Loss: 77.104   log_lengthscale: 1.075   log_noise: -4.605\n",
-      "Iter 12/50 - Loss: 72.943   log_lengthscale: 1.062   log_noise: -4.605\n",
-      "Iter 13/50 - Loss: 68.337   log_lengthscale: 1.041   log_noise: -4.605\n",
-      "Iter 14/50 - Loss: 63.714   log_lengthscale: 1.015   log_noise: -4.605\n",
-      "Iter 15/50 - Loss: 59.472   log_lengthscale: 0.990   log_noise: -4.605\n",
-      "Iter 16/50 - Loss: 55.383   log_lengthscale: 0.968   log_noise: -4.605\n",
-      "Iter 17/50 - Loss: 51.043   log_lengthscale: 0.950   log_noise: -4.605\n",
-      "Iter 18/50 - Loss: 46.585   log_lengthscale: 0.936   log_noise: -4.605\n",
-      "Iter 19/50 - Loss: 42.139   log_lengthscale: 0.927   log_noise: -4.605\n",
-      "Iter 20/50 - Loss: 38.583   log_lengthscale: 0.923   log_noise: -4.605\n",
-      "Iter 21/50 - Loss: 33.755   log_lengthscale: 0.927   log_noise: -4.605\n",
-      "Iter 22/50 - Loss: 29.036   log_lengthscale: 0.937   log_noise: -4.605\n",
-      "Iter 23/50 - Loss: 25.205   log_lengthscale: 0.950   log_noise: -4.605\n",
-      "Iter 24/50 - Loss: 20.363   log_lengthscale: 0.969   log_noise: -4.605\n",
-      "Iter 25/50 - Loss: 16.542   log_lengthscale: 0.991   log_noise: -4.605\n",
-      "Iter 26/50 - Loss: 11.797   log_lengthscale: 1.013   log_noise: -4.605\n",
-      "Iter 27/50 - Loss: 7.511   log_lengthscale: 1.034   log_noise: -4.605\n",
-      "Iter 28/50 - Loss: 3.347   log_lengthscale: 1.052   log_noise: -4.605\n",
-      "Iter 29/50 - Loss: -0.306   log_lengthscale: 1.068   log_noise: -4.605\n",
-      "Iter 30/50 - Loss: -4.388   log_lengthscale: 1.078   log_noise: -4.605\n",
-      "Iter 31/50 - Loss: -8.212   log_lengthscale: 1.081   log_noise: -4.605\n",
-      "Iter 32/50 - Loss: -12.233   log_lengthscale: 1.078   log_noise: -4.605\n",
-      "Iter 33/50 - Loss: -16.304   log_lengthscale: 1.070   log_noise: -4.605\n",
-      "Iter 34/50 - Loss: -20.214   log_lengthscale: 1.059   log_noise: -4.605\n",
-      "Iter 35/50 - Loss: -23.128   log_lengthscale: 1.043   log_noise: -4.605\n",
-      "Iter 36/50 - Loss: -25.845   log_lengthscale: 1.029   log_noise: -4.605\n",
-      "Iter 37/50 - Loss: -29.738   log_lengthscale: 1.023   log_noise: -4.605\n",
-      "Iter 38/50 - Loss: -33.471   log_lengthscale: 1.020   log_noise: -4.605\n",
-      "Iter 39/50 - Loss: -35.789   log_lengthscale: 1.019   log_noise: -4.605\n",
-      "Iter 40/50 - Loss: -39.070   log_lengthscale: 1.024   log_noise: -4.605\n",
-      "Iter 41/50 - Loss: -41.703   log_lengthscale: 1.032   log_noise: -4.605\n",
-      "Iter 42/50 - Loss: -44.084   log_lengthscale: 1.040   log_noise: -4.605\n",
-      "Iter 43/50 - Loss: -47.036   log_lengthscale: 1.054   log_noise: -4.605\n",
-      "Iter 44/50 - Loss: -49.336   log_lengthscale: 1.067   log_noise: -4.605\n",
-      "Iter 45/50 - Loss: -50.759   log_lengthscale: 1.079   log_noise: -4.605\n",
-      "Iter 46/50 - Loss: -53.638   log_lengthscale: 1.086   log_noise: -4.605\n",
-      "Iter 47/50 - Loss: -55.337   log_lengthscale: 1.089   log_noise: -4.605\n",
-      "Iter 48/50 - Loss: -57.165   log_lengthscale: 1.085   log_noise: -4.605\n",
-      "Iter 49/50 - Loss: -59.122   log_lengthscale: 1.079   log_noise: -4.605\n",
-      "Iter 50/50 - Loss: -60.640   log_lengthscale: 1.070   log_noise: -4.605\n"
+      "Iter 1/50 - Loss: 116.972   log_lengthscale: -0.367   log_noise: -4.275\n",
+      "Iter 2/50 - Loss: 113.191   log_lengthscale: -0.295   log_noise: -4.275\n",
+      "Iter 3/50 - Loss: 108.608   log_lengthscale: -0.226   log_noise: -4.275\n",
+      "Iter 4/50 - Loss: 103.804   log_lengthscale: -0.160   log_noise: -4.275\n",
+      "Iter 5/50 - Loss: 99.711   log_lengthscale: -0.098   log_noise: -4.275\n",
+      "Iter 6/50 - Loss: 96.265   log_lengthscale: -0.044   log_noise: -4.275\n",
+      "Iter 7/50 - Loss: 91.340   log_lengthscale: 0.003   log_noise: -4.275\n",
+      "Iter 8/50 - Loss: 87.705   log_lengthscale: 0.034   log_noise: -4.275\n",
+      "Iter 9/50 - Loss: 83.479   log_lengthscale: 0.053   log_noise: -4.275\n",
+      "Iter 10/50 - Loss: 79.445   log_lengthscale: 0.060   log_noise: -4.275\n",
+      "Iter 11/50 - Loss: 74.971   log_lengthscale: 0.063   log_noise: -4.275\n",
+      "Iter 12/50 - Loss: 70.757   log_lengthscale: 0.059   log_noise: -4.275\n",
+      "Iter 13/50 - Loss: 66.392   log_lengthscale: 0.051   log_noise: -4.275\n",
+      "Iter 14/50 - Loss: 62.632   log_lengthscale: 0.040   log_noise: -4.275\n",
+      "Iter 15/50 - Loss: 57.697   log_lengthscale: 0.035   log_noise: -4.275\n",
+      "Iter 16/50 - Loss: 53.429   log_lengthscale: 0.033   log_noise: -4.275\n",
+      "Iter 17/50 - Loss: 49.023   log_lengthscale: 0.037   log_noise: -4.275\n",
+      "Iter 18/50 - Loss: 44.555   log_lengthscale: 0.047   log_noise: -4.275\n",
+      "Iter 19/50 - Loss: 40.325   log_lengthscale: 0.058   log_noise: -4.275\n",
+      "Iter 20/50 - Loss: 36.155   log_lengthscale: 0.073   log_noise: -4.275\n",
+      "Iter 21/50 - Loss: 31.492   log_lengthscale: 0.092   log_noise: -4.275\n",
+      "Iter 22/50 - Loss: 27.208   log_lengthscale: 0.107   log_noise: -4.275\n",
+      "Iter 23/50 - Loss: 23.525   log_lengthscale: 0.122   log_noise: -4.275\n",
+      "Iter 24/50 - Loss: 19.104   log_lengthscale: 0.138   log_noise: -4.275\n",
+      "Iter 25/50 - Loss: 14.698   log_lengthscale: 0.153   log_noise: -4.275\n",
+      "Iter 26/50 - Loss: 10.615   log_lengthscale: 0.164   log_noise: -4.275\n",
+      "Iter 27/50 - Loss: 6.340   log_lengthscale: 0.169   log_noise: -4.275\n",
+      "Iter 28/50 - Loss: 2.735   log_lengthscale: 0.169   log_noise: -4.275\n",
+      "Iter 29/50 - Loss: -1.250   log_lengthscale: 0.166   log_noise: -4.275\n",
+      "Iter 30/50 - Loss: -5.139   log_lengthscale: 0.159   log_noise: -4.275\n",
+      "Iter 31/50 - Loss: -8.651   log_lengthscale: 0.156   log_noise: -4.275\n",
+      "Iter 32/50 - Loss: -12.557   log_lengthscale: 0.155   log_noise: -4.275\n",
+      "Iter 33/50 - Loss: -16.334   log_lengthscale: 0.158   log_noise: -4.275\n",
+      "Iter 34/50 - Loss: -19.513   log_lengthscale: 0.166   log_noise: -4.275\n",
+      "Iter 35/50 - Loss: -22.231   log_lengthscale: 0.172   log_noise: -4.275\n",
+      "Iter 36/50 - Loss: -25.844   log_lengthscale: 0.176   log_noise: -4.275\n",
+      "Iter 37/50 - Loss: -28.167   log_lengthscale: 0.181   log_noise: -4.275\n",
+      "Iter 38/50 - Loss: -31.349   log_lengthscale: 0.190   log_noise: -4.275\n",
+      "Iter 39/50 - Loss: -34.065   log_lengthscale: 0.202   log_noise: -4.275\n",
+      "Iter 40/50 - Loss: -36.747   log_lengthscale: 0.214   log_noise: -4.275\n",
+      "Iter 41/50 - Loss: -39.227   log_lengthscale: 0.221   log_noise: -4.275\n",
+      "Iter 42/50 - Loss: -41.299   log_lengthscale: 0.219   log_noise: -4.275\n",
+      "Iter 43/50 - Loss: -43.466   log_lengthscale: 0.212   log_noise: -4.275\n",
+      "Iter 44/50 - Loss: -45.630   log_lengthscale: 0.206   log_noise: -4.275\n",
+      "Iter 45/50 - Loss: -47.610   log_lengthscale: 0.202   log_noise: -4.275\n",
+      "Iter 46/50 - Loss: -49.196   log_lengthscale: 0.199   log_noise: -4.275\n",
+      "Iter 47/50 - Loss: -50.895   log_lengthscale: 0.195   log_noise: -4.275\n",
+      "Iter 48/50 - Loss: -52.518   log_lengthscale: 0.196   log_noise: -4.275\n",
+      "Iter 49/50 - Loss: -54.058   log_lengthscale: 0.204   log_noise: -4.275\n",
+      "Iter 50/50 - Loss: -55.310   log_lengthscale: 0.215   log_noise: -4.275\n"
      ]
     }
    ],
@@ -143,7 +143,7 @@
     "        loss.backward()\n",
     "        print('Iter %d/%d - Loss: %.3f   log_lengthscale: %.3f   log_noise: %.3f' % (\n",
     "            i + 1, n_iter, loss.item(),\n",
-    "            model.covar_module.lengthscale.item(),\n",
+    "            model.covar_module.base_kernel.log_lengthscale.item(),\n",
     "            model.likelihood.log_noise.item()\n",
     "        ))        \n",
     "        optimizer.step()"
@@ -163,7 +163,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x432 with 2 Axes>"
       ]
@@ -210,53 +210,6 @@
     "\n",
     "None"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[0.0100]])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.likelihood.noise"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[tensor([0.0077, 0.0078], grad_fn=<SelectBackward>)]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(model.likelihood.noise_covar.noise)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/gpytorch/kernels/rbf_kernel_grad.py b/gpytorch/kernels/rbf_kernel_grad.py
index 79d4addb5..3aea25c75 100644
--- a/gpytorch/kernels/rbf_kernel_grad.py
+++ b/gpytorch/kernels/rbf_kernel_grad.py
@@ -43,10 +43,10 @@ def forward(self, x1, x2, diag=False, **params):
             _, n2, _ = x2.size()
 
         if not diag:
-            ell = self.lengthscale.double()
-            K = torch.zeros(*x1.shape[:-2], n1 * (d + 1), n2 * (d + 1), dtype=torch.double)
-            x1_ = x1.double() / ell
-            x2_ = x2.double() / ell
+            ell = self.lengthscale
+            K = torch.zeros(*x1.shape[:-2], n1 * (d + 1), n2 * (d + 1))
+            x1_ = x1 / ell
+            x2_ = x2 / ell
 
             # 1) Kernel block
             diff = self._covar_dist(x1_, x2_, square_dist=True, **params)
@@ -59,19 +59,18 @@ def forward(self, x1, x2, diag=False, **params):
 
             # 2) First gradient block
             outer1 = outer.view(shape_list + [n1, n2*d])
-            K[..., :n1, n2:] = outer1 * K_11.repeat(shape_list + [1, d]) / ell
+            K[..., :n1, n2:] = (outer1  / ell) * K_11.repeat(shape_list + [1, d])
 
             # 3) Second gradient block
             outer2 = outer.transpose(-1, -3).contiguous().view(shape_list + [n2, n1*d])
             outer2 = outer2.transpose(-1, -2) 
-            K[..., n1:, :n2] = -outer2 * K_11.repeat(shape_list + [d, 1]) / ell
+            K[..., n1:, :n2] = - (outer2  / ell) * K_11.repeat(shape_list + [d, 1])
 
             # 4) Hessian block
             outer3 = outer1.repeat(shape_list + [d, 1]) * outer2.repeat(shape_list + [1, d])
-            kp = KroneckerProductLazyTensor(torch.eye(d, d, dtype=torch.double), \
-                torch.ones(n1, n2, dtype=torch.double))
-            fact1 = kp.evaluate() - outer3
-            K[..., n1:, n2:] = fact1 * K_11.repeat(shape_list + [d, d]) / (ell ** 2)
+            kp = KroneckerProductLazyTensor(torch.eye(d, d), torch.ones(n1, n2))
+            fact1 = kp.evaluate() / ell.pow(2) - outer3 / ell.pow(2)
+            K[..., n1:, n2:] = fact1 * K_11.repeat(shape_list + [d, d]) 
 
             if n1 == n2 and torch.eq(x1, x2).all():  # Symmetrize for stability
                 K = 0.5*(K.transpose(-1, -2) + K)
@@ -80,8 +79,6 @@ def forward(self, x1, x2, diag=False, **params):
             pi2 = torch.arange(n2 * (d + 1)).view(d + 1, n2).t().contiguous().view((n2 * (d + 1)))
             K = K[..., pi1, :][..., :, pi2]
 
-            K = K.float()  # Convert back to float
-
             return K
         else: # TODO: This will change when ARD is supported
             kernel_diag = super(RBFKernelGrad, self).forward(x1, x2, diag=True)

From cf6adac3071c99b1a70ba6d531ad41478abd9709 Mon Sep 17 00:00:00 2001
From: dme65 <dme65@cornell.edu>
Date: Wed, 9 Jan 2019 22:04:37 -0500
Subject: [PATCH 07/25] Adding comments to notebooks

---
 ...Regression_Derivative_Information_1d.ipynb | 287 ++++++++++++++++
 ...Regression_Derivative_Information_2d.ipynb | 311 ++++++++++++++++++
 .../Simple_GP_Derivative_Example-2d.ipynb     | 139 ++++----
 examples/Simple_GP_Derivative_Example.ipynb   | 236 -------------
 gpytorch/kernels/rbf_kernel_grad.py           |   5 +-
 5 files changed, 671 insertions(+), 307 deletions(-)
 create mode 100644 examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb
 create mode 100644 examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb
 delete mode 100644 examples/Simple_GP_Derivative_Example.ipynb

diff --git a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb
new file mode 100644
index 000000000..1a82f2f94
--- /dev/null
+++ b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb
@@ -0,0 +1,287 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# GPyTorch regression with derivative information\n",
+    "\n",
+    "## Introduction\n",
+    "In this notebook, we show how to train a GP regression model in GPyTorch of an unknown function given function value and derivative observations. We consider modeling the function:\n",
+    "\n",
+    "\\begin{align*}\n",
+    "              y &= \\sin(2x) + cos(x) + \\epsilon \\\\\n",
+    "  \\frac{dy}{dx} &= 2\\cos(2x) - \\sin(x) + \\epsilon \\\\  \n",
+    "       \\epsilon &\\sim \\mathcal{N}(0, 0.5)\n",
+    "\\end{align*}\n",
+    "\n",
+    "using 50 value and derivative observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import gpytorch\n",
+    "import math\n",
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up the training data\n",
+    "We use 50 uniformly distributed points in the interval $[0, 5 \\pi]$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lb, ub = 0.0, 5*math.pi\n",
+    "n = 50\n",
+    "\n",
+    "train_x = torch.linspace(lb, ub, n).unsqueeze(-1)\n",
+    "train_y = torch.stack([\n",
+    "    torch.sin(2*train_x) + torch.cos(train_x), \n",
+    "    -torch.sin(train_x) + 2*torch.cos(2*train_x)\n",
+    "], -1).squeeze(1)\n",
+    "\n",
+    "train_y += 0.05 * torch.randn(n, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up the model\n",
+    "A GP prior on the function values implies a multi-output GP prior on the function values and the partial derivatives, see 9.4 in http://www.gaussianprocess.org/gpml/chapters/RW9.pdf for more details. This allows using a `MultitaskMultivariateNormal` and `MultitaskGaussianLikelihood` to train a GP model from both function values and gradients. The resulting RBF kernel that models the covariance between the values and partial derivatives has been implemented in `RBFKernelGrad` and the extension of a constant mean is implemented in `ConstantMeanGrad`.\n",
+    "\n",
+    "The `RBFKernelGrad` is generally worse conditioned than the `RBFKernel`, so we place a lower bound on the noise parameter to keep the smallest eigenvalues of the kernel matrix away from zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class GPModelWithDerivatives(gpytorch.models.ExactGP):\n",
+    "    def __init__(self, train_x, train_y, likelihood):\n",
+    "        super(GPModelWithDerivatives, self).__init__(train_x, train_y, likelihood)\n",
+    "        self.mean_module = gpytorch.means.ConstantMeanGrad()\n",
+    "        self.base_kernel = gpytorch.kernels.RBFKernelGrad()\n",
+    "        self.covar_module = gpytorch.kernels.ScaleKernel(self.base_kernel)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        mean_x = self.mean_module(x)\n",
+    "        covar_x = self.covar_module(x)\n",
+    "        return gpytorch.distributions.MultitaskMultivariateNormal(mean_x, covar_x)\n",
+    "\n",
+    "likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihood(num_tasks=2)  # Value + Derivative\n",
+    "likelihood.initialize(noise=0.01*train_y.std())  # Require 1% noise (approximately)\n",
+    "likelihood.raw_noise.requires_grad = False\n",
+    "model = GPModelWithDerivatives(train_x, train_y, likelihood)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model\n",
+    "The model training is similar to training a standard GP regression model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iter 1/50 - Loss: 61.807   log_lengthscale: -0.367   log_noise: -4.275\n",
+      "Iter 2/50 - Loss: 59.101   log_lengthscale: -0.295   log_noise: -4.275\n",
+      "Iter 3/50 - Loss: 57.529   log_lengthscale: -0.228   log_noise: -4.275\n",
+      "Iter 4/50 - Loss: 54.055   log_lengthscale: -0.162   log_noise: -4.275\n",
+      "Iter 5/50 - Loss: 53.405   log_lengthscale: -0.100   log_noise: -4.275\n",
+      "Iter 6/50 - Loss: 51.161   log_lengthscale: -0.049   log_noise: -4.275\n",
+      "Iter 7/50 - Loss: 50.384   log_lengthscale: -0.006   log_noise: -4.275\n",
+      "Iter 8/50 - Loss: 48.640   log_lengthscale: 0.023   log_noise: -4.275\n",
+      "Iter 9/50 - Loss: 46.084   log_lengthscale: 0.038   log_noise: -4.275\n",
+      "Iter 10/50 - Loss: 44.910   log_lengthscale: 0.044   log_noise: -4.275\n",
+      "Iter 11/50 - Loss: 42.824   log_lengthscale: 0.042   log_noise: -4.275\n",
+      "Iter 12/50 - Loss: 41.261   log_lengthscale: 0.034   log_noise: -4.275\n",
+      "Iter 13/50 - Loss: 37.953   log_lengthscale: 0.023   log_noise: -4.275\n",
+      "Iter 14/50 - Loss: 34.888   log_lengthscale: 0.014   log_noise: -4.275\n",
+      "Iter 15/50 - Loss: 32.862   log_lengthscale: 0.007   log_noise: -4.275\n",
+      "Iter 16/50 - Loss: 35.709   log_lengthscale: 0.001   log_noise: -4.275\n",
+      "Iter 17/50 - Loss: 30.031   log_lengthscale: 0.007   log_noise: -4.275\n",
+      "Iter 18/50 - Loss: 28.395   log_lengthscale: 0.016   log_noise: -4.275\n",
+      "Iter 19/50 - Loss: 26.709   log_lengthscale: 0.027   log_noise: -4.275\n",
+      "Iter 20/50 - Loss: 25.436   log_lengthscale: 0.043   log_noise: -4.275\n",
+      "Iter 21/50 - Loss: 25.401   log_lengthscale: 0.064   log_noise: -4.275\n",
+      "Iter 22/50 - Loss: 21.223   log_lengthscale: 0.088   log_noise: -4.275\n",
+      "Iter 23/50 - Loss: 18.429   log_lengthscale: 0.111   log_noise: -4.275\n",
+      "Iter 24/50 - Loss: 21.253   log_lengthscale: 0.128   log_noise: -4.275\n",
+      "Iter 25/50 - Loss: 16.943   log_lengthscale: 0.142   log_noise: -4.275\n",
+      "Iter 26/50 - Loss: 16.183   log_lengthscale: 0.155   log_noise: -4.275\n",
+      "Iter 27/50 - Loss: 14.646   log_lengthscale: 0.164   log_noise: -4.275\n",
+      "Iter 28/50 - Loss: 11.569   log_lengthscale: 0.168   log_noise: -4.275\n",
+      "Iter 29/50 - Loss: 9.167   log_lengthscale: 0.167   log_noise: -4.275\n",
+      "Iter 30/50 - Loss: 7.116   log_lengthscale: 0.170   log_noise: -4.275\n",
+      "Iter 31/50 - Loss: 9.166   log_lengthscale: 0.170   log_noise: -4.275\n",
+      "Iter 32/50 - Loss: 5.869   log_lengthscale: 0.171   log_noise: -4.275\n",
+      "Iter 33/50 - Loss: 4.133   log_lengthscale: 0.174   log_noise: -4.275\n",
+      "Iter 34/50 - Loss: -0.490   log_lengthscale: 0.177   log_noise: -4.275\n",
+      "Iter 35/50 - Loss: -2.021   log_lengthscale: 0.180   log_noise: -4.275\n",
+      "Iter 36/50 - Loss: 3.194   log_lengthscale: 0.180   log_noise: -4.275\n",
+      "Iter 37/50 - Loss: -2.839   log_lengthscale: 0.189   log_noise: -4.275\n",
+      "Iter 38/50 - Loss: -3.883   log_lengthscale: 0.197   log_noise: -4.275\n",
+      "Iter 39/50 - Loss: -3.484   log_lengthscale: 0.205   log_noise: -4.275\n",
+      "Iter 40/50 - Loss: -6.994   log_lengthscale: 0.217   log_noise: -4.275\n",
+      "Iter 41/50 - Loss: -3.418   log_lengthscale: 0.222   log_noise: -4.275\n",
+      "Iter 42/50 - Loss: -1.686   log_lengthscale: 0.222   log_noise: -4.275\n",
+      "Iter 43/50 - Loss: -8.598   log_lengthscale: 0.233   log_noise: -4.275\n",
+      "Iter 44/50 - Loss: -11.501   log_lengthscale: 0.241   log_noise: -4.275\n",
+      "Iter 45/50 - Loss: -8.583   log_lengthscale: 0.241   log_noise: -4.275\n",
+      "Iter 46/50 - Loss: -9.892   log_lengthscale: 0.239   log_noise: -4.275\n",
+      "Iter 47/50 - Loss: -14.409   log_lengthscale: 0.235   log_noise: -4.275\n",
+      "Iter 48/50 - Loss: -10.353   log_lengthscale: 0.228   log_noise: -4.275\n",
+      "Iter 49/50 - Loss: -11.754   log_lengthscale: 0.224   log_noise: -4.275\n",
+      "Iter 50/50 - Loss: -13.165   log_lengthscale: 0.228   log_noise: -4.275\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Find optimal model hyperparameters\n",
+    "model.train()\n",
+    "likelihood.train()\n",
+    "\n",
+    "# Use the adam optimizer\n",
+    "optimizer = torch.optim.Adam([\n",
+    "    {'params': model.parameters()},  # Includes GaussianLikelihood parameters\n",
+    "], lr=0.1)\n",
+    "\n",
+    "# \"Loss\" for GPs - the marginal log likelihood\n",
+    "mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)\n",
+    "\n",
+    "n_iter = 50\n",
+    "for i in range(n_iter):\n",
+    "    optimizer.zero_grad()\n",
+    "    output = model(train_x)\n",
+    "    loss = -mll(output, train_y)\n",
+    "    loss.backward()\n",
+    "    print('Iter %d/%d - Loss: %.3f   log_lengthscale: %.3f   log_noise: %.3f' % (\n",
+    "        i + 1, n_iter, loss.item(),\n",
+    "        model.covar_module.base_kernel.log_lengthscale.item(),\n",
+    "        model.likelihood.log_noise.item()\n",
+    "    ))        \n",
+    "    optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Making predictions with the model\n",
+    "Model predictions are also similar to GP regression with only function values, butwe need more CG iterations to get accurate estimates of the predictive variance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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u62V6kWrFrt7QtBZSVh+j3yiXl6Z1kXL9WYuX2tBnCpanYqgzBXPJjety+R5e5cwSCcJes/KfL5dqPYYdNqo7pudF8Vi6OmFVlJKJOb1AtxLVbmgaUZtQUz0zWWNBa+el0GOnARbE33qZuby5wP1ezjkPVcw5N51d0tO6HhNlraJkdAkvl9cs4+fz1ikP+voyAjbK6+h5UbySRXc8rV25rULBLFYVCw5aWLUaK7Hca0uxBtaXpXg5D5eXwr2GzuNmH08X1l3Yy1g6t6Bag0M6Z5LyUG318xUPMCzpKY/FcqRz1RtiasXTojhvWiuabLWluHVIzuarigUHSDXwgdAsz0o2KY1yeWlam/M1V/Iay93/1XrAWoHzVf8pSrnuDBLL5UIMeyQWN2csr32q9aJ7gam5xs0fnhbFE7OFRYVVamKMQx98O6nJZMXreoFuHVYy2Uqpx66VWIn1N50z12V5IM3yDA8P87EP3EJqMrmuLMVOFvxS68zEbN4T+SvF4vKid73lcyz3fbxSujWZXt6otJ42NJMNLOvoeVG8GPd/9m858fiD3H/PXVUdr2k84yt8YLUbvnWYXxpnKXHgoDc0G5M//MOPcOyx09x/z12ecksvhzMXLbXOmEXpiXt+Yq6wbEnM9SSsYPna6V6prZ6sYj30wj1YLdWGWtaDQMM+aQ0YnxcOcdvBA5iF0mtHjxzm6JHDBEJh/vzIo6RyBqZVJOD39F5gXbBSkdvIh0KzNHnTWuAKLxcHb3rPhxe8Z3KuwGBHtEFXqGk20WiUXC4HfAt4I0ePXM/RI4d5ZyRCNuvt5C2rKPmlV1503nUG1PzWFw836zKrohrBu56MEVZRMrmMG34snUdKiRCiQVe1OqoxKjWqrm8jaGQDIE+rw/mW3w99+utc+cqDBMMRAILhCFdefyMf+sx/A8oNv56Cz72KlCuvX7uedr1epnwcbjt4gA/csI+jRw4jpeTokcN84IZ93HbwQMV71lPlAc3yHD9+nJtvvplA4GngSgKhKFdefyNPPPNcsy+tZibnCsuuM+ANMVmttXG9VDGYnCtQXOa7FMyiJ0J9Fls/53vsGpmcttbomOIqmZxnKU709BOOtmPkL8UfvASzkCcSayfR3ecesx5bjnqNVM5c8cOqRXFrUL4RrRQH1+ILvIfLXvr2CnEA2sq/0RgcHCSRSGBZDwIdmIUtRGLtxDp6m31pNTOVKZDo6ScSa8cs5PEHL8TI/xqmcUnFOuOFUL1qmiEUzCKp85Sf8xKLJdovFvrV6hsaKWXF2JkFwZG7e/mb94c4/pjhhvNIuT70jlWU5y2BWG88K4pzhsVcvjKBR0p46oFfAB7AMh5n5/5PkZ4arzhGi6vms9QYPPytdv75Tzbx4NfjC36Xypq6ZXALUD52jjgw8rcA36Fo3skzD9wJclPFe7R3pvUQQmwTQnxTCPGUEOIJIcR763n+0dFR3vSmXQDse+G7SU+Nr4u4Yuf+T09P8MJX/Srh6KPAX/LYt+/gxBORBce1MvOF+9nnwzzwtTi5uUpZMOWB71INk4tsVBaLC0+2eOnWVNasiAX/rdf/N9/4l24mRy4FvsbRI19zPXbrYe5NZQ3Xwj+ZHOW6665jZGRkzT7Ps6J4sUnn+19NMJ18Na940ySXvGiWU0+9gxve/omKY6Y94BpZ7yxmoXjg/gSf+chmnvhuO5/780G+8a+iYgdflNITbq31zvznbnJU4PMfYvtFSS558UcwjBhf/rv+imNm1oG1Yh1iAh+UUl4MvAj4dSHE/nqd/N577+Wee34bf0CyZc/bufX2Q6TXgSh2LG+33n4IIf6EQjbEr/7ZGbr64YsfG6BoO8BSOaOla/zmDIvZfMn69sD9Jh/9ta0c/r+DfOyD2yjkSzG1jcz8X0vKv8dvvu5KPnDDfRw98vtI+esVoV+tXrq1/HuMng6CfCd9W79CIPhSoAOf//fdcJ714KUr12xf+Mc7+fa3v80dd9yxZp+3bkSxacD9/9zDzv1ZbvzlcW76zRHCsSL3f7an4riZdbBz8jrzxy6T9vHlv+9j12UZ/uhLx9j7gjn+81NbOP7YiYod/Hp4wL3O/LHr33YXEOZnfzfNL/7hm3nV2+Z4+Ftxxs4E3WMMS1YswJrmI6UcllI+ZP87DTwFbKnnZ4RCMHhBnrPPqYSz9eCGdyxvs9N+HvpmnBe9doYLX5DlJ352nOHjYZ5/OAY4+SutO1+Vx6QWcoIvfmwQeJTdB/6e4eNh/ucLXe7v142luOx7vOz1TwDvAxEAPoY/8CZXSLa6lb88bPQH/9WB8MGOi/4Dy3wY4fs3itbPEgp3kejuWxfhEzNZw81f+a8v5CkWt/Lxj38cIQTRaP0TuD0riucn7zz9YBvT40F+/G2TCAGxeJEXv26Gx4+2MzPhL70v6/2bxOvMXywe+FqCbNrPT78rye+94TKe/dE1WGYM+JWKHby28jcX0ypWuMBNQ43d5S9P071JCZ6XvH4an1/y/a92VLx3PUzO6xUhxE7gBcD3573+K0KIB4UQDyaTi5fbW46tu/OcO6ZEcSPjAtcKZ9155H/bsQwfL3rtDABXXDdLNG7xwNcS7rGNTA5aKU4owW0HD/Dbr/8IRr4L+ADHHn0X8O989TNFzIKyFq8HF7yU0jWIZdI+jh7ZRs/g90BeAuIJLPMOwtG4LSSNlq4z7VTQkBIe/t84e6/MkM+e4NqDN/GGX98GdDJ8cgewPjpJzmQNPvTpr3PZS24G/gW4mVgsxi233MKJEyfq/nmeFcXzXToPfSNOW4fJRVfNua9d8xMzyKLg4W+VYlTn8ta6ycj0KuVlcWbGx7jvk7Ns2Z1m64V5O3lrF0J8A/h5AqFSZre28jeXqYxRUTD+2R+1kZ31c+Ur0+5riW6LvVdmePxoe8V7dQWK1kQI0Q58CXiflDJV/jsp5SeklFdJKa/q6+tb/ATLMLC9wOxMgLmUz/Nd7XKGRc5QeSyPHW2nb2uBwZ1qHQqGJPuvmePpB2NuCEUrW4qd9fNDn/468a73ACeA/yEYjrD78h8C/TzzQ2X1Xg8b2lTOdNs7P/j1BIW8j66BT/GSG9/Ma38+B1zMyEnlKLGKsqW/s3NfnX4mwtRokBdcl+bW2w/xxnffzote00OkzWLTzg8C62PenckaJHr6KWSvAiAU+h65XI5EIsGmTZuWeffK8awoLr9pcxnBE99r54qXz+Ivq7zcv9Wgf1uepx9oq3jvetg9eZVMwXQXFoB777ofI7+LSNthoJS8JeUXgQsxC7vcCiJ63JrL/IXikf9tJ9Jmse/KTMXr+39sluRQiORQKYTC64JoPSKECKIE8WellPeuxWcMbFf3zOjpkOdDaJz5xzTg+ONRLr56jvJythddNcfcTICh55VlvJXDDpxnWfgGSU9dAag6y2YhT9+WY0TjFo9+W21sZ/MmZgvHR1dD+dz1o/+Js3lXjl/78w/wxnffzsvfkCASs+jf9nvuMa1sHXe+yxPfbcPnk1x67az7O38Adl+W5fhjKqwgU7A831HUee7Gh3fi8+f5znfu5F3veteaJdt5UhSXu0IAnvtRDCPv4/KXpxcce/HVGY49GqWQK81eWlw1D2eycWKEHvtOGDA59uhtbphEenqCq16lbs0dF/2BW0GklXfvG4HyhaJowePfbefSF88RCFW6Gvdfo7w1T36/tBldD5UH1hNCdSe4G3hKSvnRtfocRxSPnQmRLVgtnXy2HI7V7ezzEcyCj12XVTYi2ffCDEJI1wjTysLKiZt9/LvtgI8rrkvx3jv/lWsP3sTczCh7X5DhuYdjSKnc9F5fM52xm0v5OP10hMteUhKSobBk3wszPP1gzPWEtaqVP2+Wqm4dfzzKlgvzRNsrn6kLLs2SPBsiNanCRr0+do5BJd71k1x0AK666gB33XUX9967Jvt4b4riclcIwHMPxwiFi+zcv7Bb0kVXz2EaPp5/JOa+5vWbxMs41hOnxi3iNcBRguG8GyZx6+2HuPm297B9XxbJ67n19kOAGvf1Ukjei5QvFOeOh8mm/ewrC1dy6N5kMrCj0kOjn7mW4yXAO4DrhRAP2/+9tt4f0jVgEggVGT0dAmDWw3HFzj184nFlhbvgksr1pr3TYsuefCnsoEXzV6yiJJVV43Ds0SjxLpN3/O4vsGX3Rbzx3bdz6+2H2H0gw3QyyOSIcr16/fktHzspBRdeUTl2F109x8x4kOGT6j5tVSu/Yww0C4LTz0TYdWlmwTG77c2ac586Y+1FcoYKd7UsGDoWZtfFaz8unhTF8y2Gzz0c44LLsgSCC4/ddVmWQKjIcz8qiWLtym0ejrBK9PTj820BeSU+/9cXbbSy98oMZ5+NkM8qK79V1FUMmkm5d+b5R9SEu/vA4m17d1+W5dRTETe+0uuL6npDSvltKaWQUh6QUl5h/3dfvT/H54P+rQVGT6uQAi8/v849fPzxKH1bCsS7FrqlL7gky5nnIlgW5I1iRahYq+DUfZUSjj8aZfeBLPO7GjvC6thjat30euUQZ+yefyRGIFRk+75cxe/3XaXEpbORb9VYXCfZ/MxzYeWtuHTh/Lv1whyhcJFjdgiFl710zriNng5h5H3s3q9F8aKU37CpST+jp8JceMXCHROoBIite/KcerpUWN3LN4nXKXcpjpy+AICbb/txrj1404JGKxdckqVYFJwuGzstrppHuaX42GMxejcX6OxdfLHccVGOXMbP2BlleckULM/HJWpWx8D2AmO2pdjLc28qqxJNTz4ZYecli28Gt+3NYeR9jJ5S37cVxZXzHE+NBpgeD7LrsoVr58COAm0Ji2OPKmHl9XnXuf5jj0bZeXFuQchXZ6/Jph15nrNL6rVq+IRzP514wvFW5BYc4w/A1r05zj6n1k0vGwGd2uZnn1XfZc/+tf8u3hTFZYPsBJTvuXxxUQxqgT77fBjLXr+9fJN4nXIr/9YL30mkzeKK6wZdt105O/fnEEJy8slSLUIvu4K8TN60yBSU1atYVM/d7ssXFwYAOy5Wvzv1lJrMpPS+tUmzOvq3FZgaC2AUhOfDJ1KTfuZmAmy9cPEGD9svUiLlU390D6nJZEuGUDjrp2NJnB8bDcrCv/OSrPv8rgdRnJvzce54mN0HFtcKO/bnOPNMBClbN0HNGYfTT0fo2VygvXPxa9y8K8/wiTDForfnXefazzwbIRyzGNy+9t/Fk6K4/AE9/UyEQLDIlt1Ld6HZflEWs6AeCPD2TeJl5idInno6yo6LcviWuAuj7UUGdhTcXTF429LkZcrHLXk2RHbWz64lrGUAvVsMIm0G933yu25XwvXQ0UyzcnoGDaQUTI4EPBs+IaUK3XLWkMELFl9vejcb+ANzjA/1c/89d7VkGcmSsIoSjlls2rG4cN+yO09yKEQhLzxtSHLiUs8dDyOlYNveJTY0+3Jk0n4mhlUcZiuOnWNUOnc8zNbzaJ7BCwrksz6mRgOenned++7c8TBbduWX1Ar1xJui2L4xUhNjfO8/TzKwY66iFNt8dti799PPqF1vwWzNWK/1TjpfSpDMZQQjJ0PsuHih+6ecnfuznHwq4mYFr4cGAF6k3Dtz5lklDLbtXXrsfD6IxJ4gPbXb7Uqorfwbk97N6t6ZGPZuWba5goVVlAyfsEXxzoWC5LaDB/iNn9yHZR4FruLokcO8aHfvmnTdqgVH7A0dO7/Q2LwrjywK/uZ9f8yZc+caeIX1xRFWQ/aGZvMSYnL7Ptu7ZYfrtWKzqJmsQS4jGD8XYvOupUXxZnvTNnwi7Ok1M50zkRJGTocYWGLzVm+8KYrtm/W/7vk4ubm99iS0NF0DJu2dpiuKQYdQNIPynffpZyLIoli0Ykg52y7Mk5vzMzWqdj163JpDeWzkmWcjhMJF+rctPkk55famk/cBF3P0yBf5wA37uGrPQIOuVtNK9Aw6ojjoWVHsWNtGTobp6DVoSyyMj3cq6vj8jwGXEAi18dKf/Ok16bpVCzNZg6JlW9/2nEdY2aLr3LEQRz75sZYMJ6gGxzN87liYtg6Tjp7F78FNOwsEw0VXJ7RayIiTaO5szB76nz9xvXDz2bQzjxBSVQnycCnEdM4kPeUnm/a75R3XGs+J4tm8yftfcxmTFQimAAAgAElEQVQfuGEf3/2Ph4F2Rk5+1q1xuxhCqJ3TyMmw+5oOoWg85cLq1FPKeuJY8Zdi0JmYjzutYltrotooOAtEamKMH9x/ioGds/j8ix/riAN/4FkgSCB4gCuvv5FPfvUHjbtgTcvQ3mkRihQZHw4y51FR7MRCD58IuV3s5uM0HipajwJhzMI2/OG2Nem6tVqklKSyBuPnghRyviVF8W0HD/AnP78bSAMHOHrkMJFgoOWs3tXghNydOx5m8678gkobDn6/qtxwxhbFrZYkmc6pRE+nbfroqS+6Xrj5hKOS7kHDFdBetRbP5g23nONSYT71xnOiOGX3wVaL7osBCAQfdWvcLsWmnQVGToXcElFaXDWe8qSToefD9G0pLCg8Pp9Be8c7dLz0cOtaxY3HiWX7r3/+O/KZCzEL31nyWEccWOZDAJjGXiKxdoLtXQ25Vk1rIYSyFk8OB8nYYQheI503sSzlxl0qnhggPT3BgZdtBWDfC9/J5PgYxRb6vrN2CNvQ80r4LZWLo9bY1yHE48DlBMMRfuqNb2k5q3c1pHNq7IZPhs6bewSweVeBkZOhlmxYMpM1uO3gAb506MvAJHCGo0cOL2kQHNxRYMQWlF7UO1ZRkilYriju15bixXH6YKtFdz+QxTQeX1Djdj6DF+Qx8j7ufM9vk5pMenbn5GXKJ5khe9e+HOGopGfQYNgWxab9oGgayy0vuVB5Z+77EdDG8InPndc7k56e4MWvuwKfr8jWPW8lPTWun7kNTM+gwfhwEClhruC9+2A2ZzI5EsQyfOd14956+yFu+o2fA2DnJW/n1tsPtdR973hIh46H8QckA9sXn4OdNVbKR4FLMfJ5QtH2lrJ6V0s6ZzA+FMQs+Hjie3+zZMgBKCNMLuNnOhloOVGcypp86NNfpy3xMoR4AoBgOMKV19/IHZ/7Jp2xykYNvVsKTJwLUix601LsWMZHT4WJxKwlw17qjSdFMahFt7Pvx+nfluclN751QY3b+WyyXV5nnpXcf89dnrxJvI7jjsrN+Zg4F1oy4WE+m3fl3fAJ8OYD7mWsouT3PuN4Z64EIBB89rzemVtvP8Sb3/sh+rcZdPT+OLfefojZvLbybzQcV3XPYIEJRxR7MIQinTMZP6dER++Wkije3h3jndft4optne5r4aike1OB0VNOtaPWEVdOTsboqRB9WwuLNrxySE9PsHN/DOjmmht+mdHR0cZcZJ2ZzZkkzyprY/LsfUuGHEDJRT9yMsRszmwpK38qpwyC+ew2pHyKQCiMWcgTbWvnF294AT/34p1s7y41KevbYmAaPqaT3qz64qzzo3aS3VJhL/XGs6L41tsPgdzP9n2+RWvclnPbwQPc+Z799k+XcPTIYQ5evtmT8VFexhm7cyfUBLWcK8thcFeeieGg29nOi64gL5PKGiS6He/MXsDCNB5d1jsDKuHDiWuztJV/w9HVpp71nkEDs+AjPen3pCiezRuMD6nv0rdFzT9+n+DVlwwQCwV4+d4+EtGSwhzcqdzw0FpueOdaxs6E6N9aafF+0a4efv7anfS0q+u+9fZDvOqm69XvXvt7/M5H/6GxF1snfvZle/mnD/+1/dMzFSEHFw604ytTWwM77KoNJ8MUpSTdQvdqKmswO+PDNOLs3B/lvXf+K9cevAmRnaE/HsHnE7xiX58rHp3N2/hQyJP1wR0hnxwK0relcfW+PSeKnZ1udtbH9HjQvYnPh4qPeiVwHLiMYDjCj736pzwZH+VVsgVVKxJKSXPVWoo3bS8gpSBpL0qtNFFtBMq9Mz2DP0FXf5aX3PiGZb0zoCwvk6NBCjk1U3vRYqFZPTtsy1VXnxr3qbEgs3nvbYwcS3E4WnQbJuzpbycRUULY7xMV1uKBHQXGzoawrNazFJuGqgRSXj1mc2eEF+/uoastxKv3l6rE9NnCOXk25EkPnePl6hl8FTACpAiGI7zw+hv5/iNPcvDAZq7bV9rYtyWKJHpMd0PTStWOUjnD7RD66ptfyZbdF/HGd9/OV778b+4xPe1htnapZ87ZvCWHgt4MWcqbFPKCmfEgPZsbNw7eE8X2gzlyqvqMRCc+Cp4EcRFmIU8gEqO/X5eIahQV8cTHIrQlqo8R6rMn7+QZJ2nAew+4lyn3zvgDl7NlD8t6ZxyciXncLoivrfwbi61dyhvX2a+e2elkwHNWKymVhyM5FKJ3c8mNe9GmeMVx+wcTrtWxb0uBoiWYHAm21HyVyplMDAcpFoUreAFeuKPb/fdgR5TNnSoRr3vAwOeTJIda63tUy2zeJNHdj5HfDjzrhhz093Rx5UW7ADiwpYOuWLmVP8+wXamqlaz86ZzpimJnQzPYEXG9MQ57B9oBSPSYBMNFxoe8uaFx4vgBege1KF6UYlG6E+qIHa+1qQpLMSgr1+AFfgLBS3jx624iNTnuyd2TVymfXEZPhew6itW9t3ezgRCSsbPqAfHaoup1HEuXWRCMD4Wqfuag0tIEekOz0RjssEVxn7qHppMBz827TsWMiXNBeu1NXijgq4jfBIiG/K6YdDeDQ8HWsjZmDU49pWrDtyXGAIiF/Ozqbas47uLBBAD+gAp9SZ4NeTTsRV1zJj1A/3bLDTkoZqbdY3w+wWVbS1b+/u0FkmdCdmv61hg7p0bx2JkQgWCRLnuTuauvfcGxzms+n53ges6bpRDTeZMJO45fW4qXIJ03KdqJOiOnQoQiRboGqhvsW28/xLWvuxKzEOBVN/+Rm/ijaQyOKJYSRs+Elmz8sBihsKSz32TMFlaz+daYqDYKbhzikLIwLdVZKOBbuMspxbXZGxr9zG0ooiE/nbEgsXiRULjIdDJIxmOieNYuxzYxEqR3s7qft3RGCfgXLp8X2OLSue+TLWSlc1pVq/r+8Oi3Pwaoa/bNe3b39Le7Rou+rQXGhkKYRUnWYzkBc3mT7JwP0+jmmhsuZcvui3j7B+/gvq98ueK4izbFS993S4FC3kdq0t8yXThn7c5uY2dC9G013BrxO3tjC45tDwfcuPC+LQWSQ0GyhvdKIc7lTdfDqC3FS1C+4x45qTISF2tRefm2DvYOxBe83re1tHsHbXFsJI6wmp1eujuNTwi2dEXxLyKu+rcWdPhEk3AWhtGTi3tnhIDXHRjkV1+xm939lZaLSEwS7zbdeHAvWiw0tdEfjyAEdPaZdia8t4TVbN5kajRI0RJuy+pt3YsnaW+zrcfxLotwtMj4uWDLVF2JxmK891V7OfV0DhjhB/91Nx+4YR+vf+HOBcfGQgH64up579uiSppJCWmPGSTSOdNd751krZ09sQWbgLZwgE0J28q/2dEJoZYJ93Is1uNDIfd7REN++trDix6/zY4r7hk0mBxVZdm8ZpCYtUN9wjGLtg41Z/gaUILCW6K47AYdORVe1I17zQXdXH/RAK87MMi+eTFf7u79rE7YajRuKSBb2A4sYik+ePkgb7lqGz91xeYFoRV9Wwskh0J2SSerJRaZjYLz3I2cDiF8kv5tlQvFZVvUJjTg93HD/gHCwcpppW9zwV2Y9IZm49GfUAt3Z5/BVDJAxmPz7lzeZNJuM9+9Sd37mzsXF8X98TDhoA8h1HozPhTCKkrmWsDCevRHT3DlKw8ixMXAM27C2dPPHVv0eCc8pHvQwMj7mJ32M+fBDc3EcKULfnt326LH7uhxrPzquH/5q09zZmi4AVe5PKmcQbEIE6MB93ts6YwilhCJmzqUwO/qNzELPu5873s4cfpsw663Vop2paLxcyF6Bw1XDzhjtJZ4SxTbFqu5lI/0ZGBBkl0s5OeaC0oJAy/Z01thdezsMwkEiyS1pbjhuC74JbrT7NsUZ7cdC7Wjp21BEkv/tgL5rI+ZiQBF2RqLzEYgb1quy3TkVIjezQbBUGlD4hOCq8ueuUjQz+Vl8XmgFhnHUuw1a4Wmdnpta1Znv8l00nuuXMdSDNA9YBLwCfrjkUWPFUKULI5bDJLnWifBNNbZazfkuBDhex6zkKers4Od27Yserwj/LsH1LVPjgQ9t2aqDY0zdue38m/vUZuAzn4DIZSF+d67/7olahWncyapiQCW4aPH3pgNdix+DwJstmP5u+zvfPrpKf78/3xk7S+0TmQMi6KUTAwH6SkLndjTvzCGut54SxSXuRCABXUWL9vaQbAszqsjGnRjvEAFnvduNrQrt8EUi7KiEHcoUqSzr/Jvf9XOyhbAV+6o/NlxaTmB916bnL1KeUzd6KnQgrCXnb0xtyyVwyWbExU/920tkJ4KkMsI/cxtQJz4xs4+k/SkH9PAU3HFc3mLqbEgQkg6eg364uFFQ7wcHCtd7+YCUyNBLLM1PCTpnMlU0gB6efnPvIhrD95EPj255PGOsHJF8aj3mkDM5lUFg7aEpUK5IgHikcU7lmxKRLjt4AF+8zX7kPJ5YDff+ffD+P2+pvc0SOfKLN62SBxILC2KO2JBbjt4gH+6/TX2K9s5/Om7EUI0/btUw1xexVBPjQZc70xve4j2cGDNP9tboti2NpY6C1Xuvi8Z7Fjwnv3zFuheOz4KdPhEoyhPkBw9rZLsyr0+A4nIAstLfzziul1BufAAt0SL1yZnr+JsRIsWTAyHKso4AVy0KbHgPZ2xUMXYOZaNydEghiXJGdrKv5FIRIKEAj46+wykFMyMBzzVxCVTMJkaC5DoMQkEcWNtl8IRK71bDIpFweRosCUsxemcwatu+igAey7v5I3vvp1//OfPL3l8NOSnIxp0rY2To96rYjCbU5Zi5zs41VAWw+8THPryt1WIie8YcCHBcISfefNbm97TIJ0zmBgpiWIhzi+KAf72K9/h8pddbP+0g3Akyi233NL071IN6ZzJ7LQf0/C5lTa2di9MKlwLPCWKnd12ciiEENJdbEHFrXXEFu4Ad3THCAVKX7Nnk8HUqHfbjXqR8gTJsTMLrY0XDS5MigTYV5Ys2dVnIoR0JwYtihuDM3ZTYwEsU1R0FvL7xKLZzwB7ykoFORVipvTYbVi620Kud2g66S1xNZs3SZ6V5DJPkZpMVi2KHQvr1Ghr1PgttzY6VTQGlggDcehPhInEJG0Ji6lR7zWByBRMZW0ccKyr5x+7i3fvUCEmxeeAPRj5POFYO5s2bWrA1S6NExstfJKufoOuWKhC1yzG3gu205bwAdMI3y4K+RyJRKLp36UaMgWVlAvQ1W/H8Z9nQ1NPPCOKnXIyoCzFnf0mgbLYxl29i8eaBPw+t4A8qBibQt7H3Iw32416EcfamM8KppPBBeXYdi8xdjvLQl8CIUmiR01woDc0jcJpljN+TrnAe8vqRW7pjBIO+Bd9nxOfB2Xu1zG1IOux23h0xYKuKJ5KesxSnLcYPpknn3mC+++5a1lR3B4O0Bb2V4QdtIIoTpU1Q+jeZBIJ+hc1JJXjePC6BgwVU+yhZzdnWBRMyeRo0HXBLxUL7jCQiJCenmDXZR1AG1e9+lcZGRlpwNWeH2dD09Vn4g+ohM7l6GkLkZ6eIJaY4YJL3sBPvOkdLfFdqmE2bzJlrxfOvLHpPDHU9cQzong2b7rJGePnSvUiHXb0LG1aLy+yXj5RaVduY3DiUp1uPOWW4u620JITc297mHikFEPUvcnQluIG44YsDS0MWdp+nmduIB5xLRntnRbBcNHd0Oix23h0xkKVDTw8cg9Eo1F+5bo95DNdwCmOHjnM5s7YsnGZve1hOvpMfD7ZMmLSCSVIdJsEQ7IqYdVrx4N3DxhMjgbIeKj6xFxeueCNvM/1Vi1v5Q9z6+2HePXNrwbgmp/4bX7/r+9e82s9H0rcF5kcKSWd9VY1duq7XLC/h+xcF7d84A7uvffetb7cupDJW66luLPPdEN5GoFnRHG5ZUHV6istzqGAz834XYxtZaK4q8ylBXqBbgSOpdhpvlGeIDm/K9R8yq38PZtMxs/5OPTBt3Pm7Lk1uFLNfNzk1nMhguFiRWtupxbmYvh8ws2OFkK5wJxnzmtlnTS10xULEYlJou2W3cDDG/fA4089y6XXvgMIAacIhSNVxWX2tIfx+9WCPjna/KoNBbNIzrCYGClZTZcTiKC+ByiDxNRYkLm81RLVGKphLm+VLOMDBu3hANHQ4p4th45okHDQ5xrdxlugvbXz+RPDQTe3pneJ+sTldLeF1Nw7YDA16p2NKMCcHT4RCBVp67CWrMe8FnhGFDuJWnMpH5m0v8KNu7kzsqAYdzk9bSG3dmq3vWOcHNWu3EZRniAphKxo2bhUeRyHrV2VG5rURIjjjz3C4b//q7W5WE0FjpU/ORS0222r10MB37KWpvJarl0Dpn7mNjBdsZIrdNpD4RPxnj4EOwDwBYYxCvmq4jIdC2uXncMyVzCbWobOSfSbHAm63tJqRHEiEiAUUMlORt5HaspHxiPe1dmy+tJdA4ZbBeV8CCHobQvT1W/iDxYZP9f8joSzeZN8VpCeCtCzSYn1ar5LKOCjPRygs88kN+dnaqbomQ3NbF6Vb1S5RNXdq/XCM6LYoRTbWLI2LheALUTJahVtLxJps9wFWluK1x4nLnXkhIUvMEZ2dsz93VJF8B2cOKLbDh7ga5/9DdQtu5Vv/Ns9nikv41Uc6xIo70xvWZLdQOL8G1GgwnvTbVsrAM8l62hqxwmR6ug1mUkGPHMPzOUtZibUtb/9t97H69/2c1XFZfa02RZWO+xAyuauNbN5E8tUoSulElfLCw0hhJ0kqd4zM+6d5iuZgsnMuBq7rn7TtXovR288hM+vkrsnR5ofD57OGa7Fu2fQIBTwLSiDuRRdsZDr3ZsZD3hmQ5PJW0yNBei0k+yq2QTUCw+K4oWxjecrs+JQnmVbvkB7xWLhVYpF6boOjz8+jWU8yf333AUo61EsdP66gz1tKsv2Q5/+Ohde4VhnLiAYjnDTzTd7oryMV6koxzYSrPDOnK9wvEN5yaCufoPZmQD5rK5VvBEJB/xEQ34S3SapqYDbEKbVyRRMrrz+nQBc+IKtfPjP/qqquEzHdd09YJCaCGAWRFNFcTqnLPSyKOjeZOCzxW41dMVCdPSWhJVXGifNFVRcaihSJBIr0h2r7vs6m4XuARUy4sT0NovZnFlRjq3acQPoaguScETxhDc2NFKqbnbTyYCbZOdsMhvB2ldCrjOuC96OrRECBjqW/4P1ly/QA6XSNNpSvLak8ya/8brLMAt5YAT4X44eOczRI4cJhsL8fD533vcLIeiPhymY/cS7ZgHw+S/ELHyDaCzuifIyXsWxkCxWjm25Gpmg6pwmokFSWYPuTc65gvR1e2NR1dSXjqhK8pqd8pPOemPezRQsZiYC+INFYvGiGwayHI7runvARErBVDLQ1LjidFnliZ5NBh3RwHkbkJTT3Raio3cOUMLKK5vaTN5kZiJCR69ywXe1VTd2jujsGjB58geqAtJs3qQ70DhrZTnpslbV3ZsMutuq7+qmLMUZAM/UB88ULAxDkpoI0NWvxm4lG4Fa8Z6leEjtWp1Ws12x0JJlocqpaAQxoGsVN4pU1uBDn/46l7/szcAAcIxgOMKV19/IfUcfqeoczoamkH8OhMXVr34/1x68iXMjrdGXfr1SigW3Q5bKvDPL1ft0cGMr+50EV29YKzT1pyOqrFbFomByQmBazbO+Vctc3mRmPEBHt4UQqopGtXS3hejaVIrlnc03r4HHbL5kbezeZFQdSgDQ3RYk3mnh80nPCCtQ33lmPOCGD1QrrMpDX9KTAYyCaGrzlVnbyh8KF2lLFFckEDtjIddSnJr0RtjSXMFkZiKAlILOPpUguVxN5nriOVE8MRysSNSqpqwMqK5KkaASz139Bvmsj0za56kSM14klTNI9PSD3AWAL3AKs5AnEmvn0j07qjqHk3n6Cx/+G7oHLAr5Tbzx3bdz5z/es2bXrSlZit1ybPZz1xb2L9kqdT5OgkR3WVc7s6hLIW5EEpEgCdtL8Inf+wNOnBlq8hUtT6ZgkZpQ3ez8PkF8BW1mu2Khim6OzYxNdeJSfT5JZ7+5ImHVEVUxtvFu01OW4qyhrPydfSbhoG/ZUD2HaMhPLOQvVaoaa25ccTpnMDMepMNOOqvWWwFqIxqJScIx9bfwwoYmk7eYLqtR3LWCjWg98JwonhwJLuhkVy1uzUXHlTvaGvUj1zNO9YKppHJD3XLbO7n24E2kp8bd8ViO8szTnk0lN6BXJmev4sQUT4wECYRK5dhWkgnsbGjiXRb+YNEtyK7HbuORiAaId6lxH3p+io/88R81+YqWZy5vkpoI0NFrEo8Elk0uLaczpizjPr9kajTQ9ES7yRHVQMXvZ0VCozMWRAjo6PFW5ZBUVlmKEz0mndGVCauutpBbqWqqyXWmndjoTjuueyXeio6oPXbdFimPWPnnCiZTbjc7s+qwl3rhKVFsFgSpyYC7gwPoa6++y4lT8Lq7rJd7xgPuBC/juJ0ue+m7ALj4mi288d2385v/9xME/NXdft1tITf+rWvAcIt663q3a0tFGSc7tgtW9sw5Fimfr5TNDXrsNiLX7NnEx95/nf3TJj599z+0fAWZrGExM+kn0WOuuHlAd1vIrlWsrLTN3Aim7cYdjsdmJUIj6PfRFlIbg5kJb7jgTavI5LigaAk6ek06V2BdBeiOhVydMTnWPCu/k+Q3Mx6go1eVxOxcwX3o9wnaw6Wx80LoWrZgMT3mNO4wVrQJqAeeEsXO7qG7XBSvwGrV4wbQO4u97mq31pS3CW7vNInEVCx43zLtNsvx+4Q7qXX0mqQmA1iWLu211jhW/snRoBsbCapkUbV0xco2NP2q9iTosduIPPTEM1zx8hfYPw0SiUSraoTRTCamiuQzfjpWIYod62S3XaO7WcIqb5Y6ormieIVCozMWVMLKIyXZMobFzLjSCx095oqEJJSqNpSs/M2JKU7nTIoWbhhIPBKs2pjk4MTypzwSPjFXsBg7a+Dzpchnx1Y8drXiKVHsuM2dlo1tYf+yHWrKcZILYvEioXDRrWHohRvFqzjJWhPnKkt69a1AWEEp+aGz10QWBenJgLbyryFWUbrCdWos4CbKQXX1TR18PuHGwDmLKqDHbgNy4c5txOJhYArh20o+X10jjGZRLEqSo2pD19Fjkljh4hyPqAoPnX2Gm6AmZeObJ8zmVI3i9JTfbZnr5NdUSyIapKPHbgKRav0EyUy+JIo7+4wVj11XzLby96qwk2ZVDnFaVbsW71UIxIQtimcm/Z4op5fJmxx79CzF4hnuv+cubSk+H1NjlZbilSzOULIUC6EW6Olxx5WrF+i1QErpxmKNnwtWNFxZ6dg5bvgOu27h9HiAWe2CXzPSOQMpUXWFZwLuM+f3iarrfTp02WOX6FEuvGJRh09sRIJ+H5nUJNH2DHsO/BSvv6m6RhjNImNY7hqR6DGrbpjg4PMJEhFl4ZuZCGAYsikGmNm88q452fyrEVYd0aBbq3hsxNfylUPmCqX1vaN3FVb+WFlHwrEg6SZphNl86Xt0riIMBFSCa0ePiWX4SI619rhFo1Fee2AzkyMWMMzRI4fpaQ83NMTKU6LYyZ51Hs6V1q6LBFVWKcy3WukFei2YzavWpmZBMDMecBMcYfWiuLPX7qyU9IYbz6uUahQ7ZZzUz12x4IqSjdR77LHrMyhagrkZv7YUb1B+4/9+gq17ujAKXbzrdz5SVSOMZpGxk+xAWYrjkZWX9e+0G18ULUF62t+UhC2ncQdgx9eu3PLmCCvwRgOPbEFZin0+SbzTWrGluCMaVA1O+lVHwrxRbEoDDzV2tqetb5WiOFoqSzc6Klq61fPx48d58atfD2wGzhEKRxoeYuUtUTyqypL4bc/PSoUVlBZoJ/AcdAOPtcKJJ55KKiuFUzUkGvLTtoLSRlBKDHE2RNPjAbKG1dIPuJdxKk847dC73HabNTxz5YuqthRvSOKRIPFub8Q3Oo07wLYUr9LC6nTlmk42J67YqdcLq7c2dsSCFV3tWr0j4Vxere/xbpNgcGWl9MBOUIsE6BpQ96ppNEcnzB+7lVq8wS6F2OPcgwFyZuuO3eDgIP5IG7AJ4RvDKDQ+xMpTHe2mxgIVSXar6XLS1RZiaDqrErZsV662Wq0NTvWCKUdY2aJ4VeMWU21TY/EigZDKxpVSuTjbVzjhaZbHLaVnt0PvrmXs5m1oVHxl84rha5pHPBxQrZ4n/S2/MXJEcThm0dYObSvIX3FIRIN09mUBJUiaEao3W2Yp7uxbnbCKRwKl53ei9fM5VKKd6mbXHl5ZKT2HrliQ7gEDKQXTSRVX3MjOaqDE/fR4FH+wSFvHyi3eYI9deavnglV1zeZGUyxKJkbzQJjr3vCTtPkfb3iIlacsxVMjwdpFsZP002NimcqV2+qTs1cpVS+YHwu+8nEL+lXbVCHUjtkNfdFW/jUhXWYp9geLxLvUM7KaZ87Jwi+38re6lVCzNrRHlCg2DR8Tk63t5ckado3ibot4JIAQKxdWHdEAnX12yNd4c2oVzxVMZsaDhCJFIm3FVYni9lCAaAwiMcsTXe2c8ImO3tVZ+EFZ+cs7caabUIEinTeZsWsUC8EqNzRBOnrUeKUmWtvKnzUsDv7iXwKwbW83v/vHf9HwECvPiOJCQe1ynMoTsVVk0AIVpb3AdgUZWlitBeV1bn3+8ljwlbvgoZT80NFXliTZwg+4l3FjikcDdPWZ+OyZYjXdhaIhP+Ggr6JVrA592Zi0hwMk7AV6bHTlIrORlHezq7aD43wS0aDr3ZpONkcUp3NqvqxFWPnscAK33m2Lz7sZRxSvMhYc7OTC8sTuZoS+2GPX0auqhoQDK9c8fp8g0e6jraP1xy5TsEhN2iFL3auzjNeKZ0Tx8DmBlMK1Nnat0o3RMc9qpeMb147yuNTOPsONBV9p9QIHx8rf2Wsy49S71ZbiNaF87Jy63j4hVtRitJzOea1indAXzcai3Q6fAJga97V0jfhMmbWxFmElhGpcM51skrCy41I7+gxCAaNi9kAAACAASURBVN+K8zkcHDe8shS39rw7NWORy/iVpXiVG5qOaNDtIjcz3viudqZVJGfXW15tPLGD02I91eKhL5mCCq2C1VV8qQeeEcVnTpU6msHqLFZQ1rLSQ/FRXqVkbQy6LTMBulcRPgElC4djrVDx4K27qHoVKaW7eE+NlcYuHgmsuHC8Q2dZ2JIOfdm4xCMlUdzqrtxM3iI16SfetfrFORxQHk1V7SjY8KY1plVUHcJsF3xileIeIBEJEO+2mJ1q7XEDGLXDUBNd1uo3NLEgoYikLWE1xco/mzeRUlmpO/pqE4jtkQDxTpP0tL+lx67SUrz6zWgt1EUUCyH+SQgxJoR4vB7nW4wzp5Uodi3Fq7RYOS0rE10mPp9kOhkgWyg2paj6esdp3DE5GnATtUIB36oT4xxh1dlnlOLB9Yam7mQKFmZRYhRUkxQnrq6WJJP5GxrnczQbC2UpVuOengq0tLdgYqqIafiI1yCsQJXE6uxT7ekbXX1iLm9RtNQGpJb4WoD2cJB4lxJWrexdLRYlY2NKL8S7Vy8mnfd19BpNEcXpnMncjB/L8KkNTXT192B7OEB7l9rQtPK864jicMwiHJXeFcXAp4CfrNO5FuXsGYHwSbe8TS1dTjqiwQpXblE2p6j6eiZbsDAsVaM4NRGo2cIPuBO649KaTgbItPDk7FWc0Am3WY69oVlNKSeH0gJjMp30c+iDb+fU2aEar1SzWhphyFiMgN9HR4cgECwyO9PaVqvRUWUoiXeZNVW4SUSCbgOPXL6x9W7TeUN1RCuKmkVxPBIg3mVh5H1MzrRuE4isYZGeUuMV77JoX6WwigRVLkRnk0Jf5jcgqclSHA64G5pW1jpZJ46/2yIW8q/aM1kLdflEKeX/ApP1ONdSnD0t6Ogx8dv3d00LdHQRV24L3yhexBVWdo3iWi38sLCKgSoiry3F9cYNe3Haqvc7jTvqYynOZwIcf+xJPvaXf1rjlWpq4FOssSFjKeIRP+2dFrMt7sodT9rWxs7VCyuwWyTbDTz+5v3v4/jps/W6xGWZy1sVHdFqdsF3OU0g6nJ5a0KmYJG241LjXbW54J060zPjQbKGhdXA5ODZnKo8AatrVV1OeyRAe6fa0Ey38IbGiSlOdJs1PXO14J2Y4tMlYSUEq2pV6eC4ISpduVpc1ZP5NYqduNRaLPyhgI9oyF/ZwKOFF1Wvkp7XuMOxFNcqim87eID/uPv99iub+dI9n0QI0dAWnhpFIwwZS9EWDriiuFXn3YJZZGpCCav2Gi3FcbvVM8Dppyf4yB//UV2usRpm80ZFR7SOGlzwjqUYIDkqWjbkMFuwSE8HEELS06tCJleLY+WfS/nJZ0VDrcXzWzzXEg+uLMVq7EZGW3PcQFn5U5MBO5648Ul20EBRLIT4FSHEg0KIB5PJ5Irff/aMcMuxtYdXn/AD8+Ibk9pSvBbMzK9R7Airttpu9EQkSHunhc8vtaV4jXAbd4wF8Pml2w2powYrf3skwO9/5r+58Irt9itbCUeiDW/hqameWufspWgLB2jvsJidVqX5WpFswWJ2Soni7t7iqsp/Oly7d5C7/+B19k9buOeT/9iwzWA6V9kRrRZLcdyOKQZITfrJGa1pcVTCyk9bh0VHW23WRmXlt+tMTwSYbeB641QN8fkl7Z1WTSIxHg4S71TX7sRbtyJzeYu0UwaxSU25GiaKpZSfkFJeJaW8qq+vb0XvtSxIjsGxR79AajJZU2kSqIxvzGXUDrBVLRZeZX6NYkdYOSEQq6UjGsTnU26x9JTqSW9arTk5e5VU2dh19qq26n6fqMlS4fcJNm8epL1Ddffy+XdSyOca3sJTUz21zNnnIx4O0N5ptnT4RNYoWRsH+moTEQ88+hQHXrrf/mlbQzeDTvhELR3RHKIhP109aq5Nt7CVP1MwmZ1SltFahVWizMo/PdbYuOK5vIpl7ugxaYv6CQVWL9fawn7XUjw1IVq2RvzkdJFC3keiu7aQpVrwRPiE3w9vvunXmE6+h/vvuasmFzxUxhSDrlW8FqRyjqW4skZxLbHgUAp9cWougq53W29SZeXYnNbcqt5qbeIgEQmSzx8D4KWv/yDX/8wtDW/hqWk+TvhEKyf9ZApKtMcSFokarY17dm6nLSGBLMK3g0I+37DN4GzecDuihYO+mizeAP19KuE9Pdm6VQyyBYvUlJ94Z21hL+C06S5r4NHArnbpnMnEsCQz+yTWXG2RTgG/j54+JYRTk63roRkbUWtMorv2sVst9SrJdhj4LrBPCHFWCPGL9TgvQDQaRQjBPZ/6eyDD0SOHueGSTTW5nuLhAD4hXOtlqsXLlHiR8phiJ544usouhOU4Vn5lKVbnalVrk1cpxRQH6O6vvfKEQyIa4Jfu+AtCkSLCN8ib3/Phhrfw1DSftrBKtDMLPqZaNOlHVTDwqyS7GhfnaMhPJjVBpG2avS94K6958zsathmctS3FHTXGpDok2lToS3rK37KNVzIFVXos3m2tulGJQ9zu4gcwnQwy2yDjmbQrYg2fzJDPPM2RTx2q+ZyDA+r/rboZNawiE0klST0viqWUN0kpB6WUQSnlVinl3fU4L8Dx48e5+eabXREcDEe48Q1vqcn15LSsTNjuhPRk67qCvIoTlzoxEiyV9KpDy0YnkzjRbbpFvnVXu/qRNy0VkmJgl9Kz44nrMHbuhqbbJD0ZIGfo+uDNYi0NGcvRFg4Q77QTtuoXqlxXsgVV1iveVbuwAnj/n32CrXs6KOS6eccH72jIZlBKyZwdl9rZW5/EJbe0VwsbkjKOpbjGyhOg5qxQWKoWyQ1s9RyNxXjvq/aSm+sAzvLVL3ym5jj0jvYA0bjaMLTihiZTUMm3QM21wWuh5cMnBgcHSSQS5PJ5AqEwZiFPd1dHza4n1Z1H3eCt/IB7kbxpkTOsBTWK62FtjLvCymJ2xk/R0kmS9cTZzEwngxWl9Oohip0YsXinEhxFKVvWjbfeWUtDxnLEQiqmGCDZokk/KqbYT3unWRdRrCpQNLYJRNawMC3pWorrITKcKgbpKX/LPruTU0XMgq8uG5pIUMXydvbatYobFD7xwCNPcuClbwXagLN1iUNXm1E7lr8Fxy5nx/EDtHfWZzO6GlpeFAOMjo7ys7f+Eu+981+59uBNTI/Xbl6IRwLE4kX8AalibLSwqhtunVu7skdJWNUWCw7lMcUmsijssk567OpFKexF/Z1LG5raxy5eZilO2aEveuw2Hm0hFT4BMDPlI2+23j3gWK3au2oPnwB17zsNPGZzVkM8JLNlHdE66mQpVglbJjMTgne+9WBL5gSMjJSarrSFawvXg1KynRLFjblXY119CKEq9fgCo3WJQ293YvmnWnPNdJ454ZN0dcuaSunVQnOk+Aq59957GZ7J8vkfnOHmD/whv/aKPTWfMx4JIoQdmzqpbhIpZc3JRJqy9s4j82sU1z4phwOqy5CTSaviwXX4RL0oT5CE0tjVw1Lshr50WRx7xE6SzFvQXvOpNR6iPOlndtpPrlAkHKhdvNSTmbRFPqOStdrC4ZrPF4+oKgJFSzAz6SNTWHtLWEWd2z6TRDRS8znb7PCJ1KSPR8e/zx133MHf/u3f1nzeepK041LjddrQOM1XTjwRZS5vNkQnzOVNZibUnPvW9/8qocl4zRuQWEhVoDh3PEy2kK3HZdaVrC2K2xIWiVjzpKknLMXl1GNxhtICHe+2SNmu3Fatu+g1FgirTfVzwYPa0LhJki2cBe1FHEvxyMkCYCH8wwhBXZJ03GeuyyST9mMWBBlDb2g2IgP9SlS0aq3iUVt/xLss2kL1CTvo6CuvdrT2971T5xaom6X4ip0DfPMLf4wshoA4H//4x1uqAU+xKJmwOxF29FhEa0zshlLoSybtJ5tpjHdrNmdy9Q3vAWDP5Vs4dNddNcehl+LBWzNJMmuo0I72OiS31oLnRHEtxccXO0/CthSD7mpXL8pd8BXNH+olisMBEnYRecfKr6kPTkzxkz84CQzxjc8fqrlZjkM4oOLz4t12gqsOfdmwdMb9RGIqbrclRfGoElbtnRaxurjggxUlQBsRV1wuijv7jLoIjR898TQ7L95s/7SJWCzWUg14soYycgEMDNTHouuEvoAqy9bIDY3wSbp6LdpCtd+DMTvBNTfnJzXXes9ctlBkdlpdY7PiicGDorhewqq9zGqVntJd7eqJI6wmR0o1ikMBX91u9Hgk4AorFQ+uNzP14meuvoAP3LCP5NkicIqjRw7zyy/fXTdLUCJStqGZat3mDZq1pS3kp81u9dyK94BTFaO7tz6hHe3lpb0aJYpzKg7W55Mk6tDIAmD3jm3EEjkA/IFt5HKt1YAnU7AYH8oBFhHfeF3OWd6meyYZIN0gUTydVEmNibZAXcR9eSz/cOuFgru1wdvrUF+6FjwnimvpyFOO80cPRVOkpwTT40ktiutEqc5tqUZxvcYN1M49GJJE21s3acCrfOTz3+TKVx4ELgBOEgxHeOXr3lA3S1B7edUXHfqyYXGsVq2YCW8VJdOTamns769PQlw8EqhoT98IUTxXUNbGRI9JIhbA5/t/7L15nFxXfSV+3l6v9urqbqm1WNZm2ZYtG2FssMNmQECQgWAcvDBJnIV8kkCYcTKeJUz0SyYTkklCQjB4QkKIwY4DSZwEFDCO2bGwsfFuy5bkltTqrfZ9fdvvj/vuq1fd1VIvb2tU55+2pO6qct+q984993zPWTuxEnkWancKAPDTv/BH+NVf/dVADdu1FQ0v//gEgCy++oVPOfKYUcnWapcTPIll60XpKY7VHVM/OABkso48pKPoJb5oCDugjK8W648UO5RdR6NWpo89DIDD1/723qF9wiFUWjZS7LCfGFiQVVwgnsSg1lauJ6iaDiGWhhSKA9gMhp2B2u0gmVh7BCJFVBKGQ5JDENUqoRFPccA2Rs1u7/Rw4wZnBqoEjkVY4pBIq541qNbbZNAuOeas8vYbv/87AABevACf+OSnAlPAI8syLkhHkD1TB5BxJNsXICKMXeX3wj5RM4ckE2POROkB5D2YGiVzU/kAkuJqnVg7hp7iFcIpxVGWZXz4+t2YfP6rAIAnHv4h9m8bCczAwHqFoulodhdnFDtJiukHhuRl8jAMBE5tWo+otVUYBlDMMgB4vPWWd+Dag7egVnKuYSFqK26oD+0T5y1I1XMwM1Npm10orCEZd06xohaKSl7wJO+21lFRyQmODdlRjI+zYFgD9UqwPr+Tk5N453veD4YZB5BzJNsXIDMs9gIPt+0Tiqajo+hWPXfUwRKLsTHytVQIXspW1rzNEB//kBQvG07tmiYnJ/H6d7wXnFACAHDCBbjewWPi8xU0jm1hRrFTCj/QI8Wk1W6Yd+sUqqbt5S23fBwAsOOyUdz4kUP4u7//smPPEQvx4EUDckwb1qufxwib/sZ6hfNENV0J2l0djSrxPDuRPEERkygpdj/vVtF0tLs6URvTzhKrWIhHJKahUQnWhmZiYgJSOArDGAHDlhzJ9gVIA25EtGUVu2yfqLdVtBss2k0OiVFnVf4NY4QMV8tc4PLBc2ZqSCypIurg526lWBc5xRQhgXMsz5I25WnKMwAATUlDDOuBGRhYr6DFHTSj2MmaYAprSNKsCzYMBEqxWK+gA5K0uINaX5zc0NBhS5r6EqSb6hDegbTaaTB0Bvl8sKxPLYUQvkjcWW9jVCIE9eiPIqi1vCFW3TbruH0iLHGIJLRAFiflc1nwwiZcdq2AvTt+0TG/cyzE21rtXF67Dil5AUi+dCy09nxpivQIC04gm74g5YNruoFi3tnEl9ViXSnFtM3MKdTKBbz2nW8EAGy7+B0o5DKOPv75iKptyA4A0pRYOUiKBY5FSOAQH9HQ7bDoNNlh3q0DWLh2yTEVHMs4ekNdaH3pqjoUbZgPfr4hLHKWjSbnnDvHEbQUDc0qIcVORkNFzRSDbptFuWK4+r6n6QUAkBhVHD+piySIyh+0vNv/+sd/A1WJYnxrHH/2F875nan1pZwTPCHFdO2SoyqikrPWw0hcD5xtqaWQTRYApNK6b212wDojxU76ogDgU5+7Fz/7X/4HQmENF1x8AL/2B3c7+vjnI+iQXSljRgGZ2ZxO5UtTREO9SdrqMIHCEVjWl4yA+IgKQTQQCzkTB0RhkeJh1fN5DVngrM9v4EhxVyP2CZeUYsD9Ao+FbXZO2idkgQxJBs0+AQC5Ajl1iCScX7vkmIpGlUO9YbhqPegvXVEcs4wCZDMaSZD3d5DWjrTZ0eFWf1/LuiLFTu52AfsNmqhWw5vz2mFlFGdI4DnHkQ+iyDv7VotJfF+r3dA+sXZU2z1S7MaAJADIIgeeZRBPaagVyedvuHbnH1iWQXqU/Hcxz8AwgmOhaCuUFOsIO+htjEq9rOKKy8fw9Y6KzGlS5cvxWUdPe8iQpBa4QTsAyJnRxNGE5ujakQQKck2s5N31FdfbPVI8MqY7Su7DIo9oQiUbmgCtXdscbhUkHamkv5aOdUWKnVaKe0e5ZGCrq+pQh0e5a4JFrLLuJE9QRCQecTPaa9hq5wysDU2Wtw1IurN2sRFyjNxpMcNYtvMUG0xFqFbm0FGDc92t1DV02yxRGx30NpIjeJNYFdwnxU995xkAOh578JOODgxGTLWxWeVQbwfrulswSXFqxADnQC4zBfUUA+43ElKVP5pUEY+yjp7UySI5AWlUObQDNGjX7GqBqHgG1hkpdlopjthSDKxWuwAdKaxHVFsKqoUspl6uIZqsAXDWT0wRkTirBKJaHObdrhWqpqPRVaHrQDkrWAOSbqxd1BYiXx0WeJy3GBtlwDBG4LKKsznzCD6uISw4bZ8g/59uqo2yLOP6izfgzLESgAx++O/3guNYx+JGZTNj2jCCNyRZLJKvo6POPm5UIpnBAFDO8dZAuRuoW8UdzmUUU0QkDpGETvzgAfrMEU8x7/uQHbDOSLHTSnFY5MCxDLFPmNFeQbo4rzd0VZJR/OAX/wqaMorC3A8AuKM2xiQB4ZgOTtBRKwXrproeQTOKq0UemspYSrHTF2WANiuZKn8pWN62IbxDTOYQjgev1Y4ewcdTOngHB35CAodwmEEkobpa9Tw5OYlr3/4eMMwFAKYhSiFH8nopImZyCABks8EhxR2VNKIBwPiYszm8US+VYrOeOzHm7JAdAIQFYp9o1TjUW8H5zLVMpTiWVB091VgN1lUkm9M3aIZhEBY5xFMq2k0O3TazrlWrjqpB5Jw9blkJ4tEIOp02SEUwh+njh3HHgfdDlCR02m1HnysicWAYIJ7SUC0M7RNrhX1AEoBlfXFN5U91AJCq5+GG5vwEVRxrASPFxQL5Opp2/rGjEiFXhFg1nX8CkLhRXorAMDaBYSahdJ3J66VgWQYjaUKG8/nglEC0uzoaFbOee8zZx46KPEIyEI5pKOcF1NsdZ5/AhK4bZj23gO17244OSAKmfSJhbmgCpPK3zfSJTTs7vlY8A+tIKeZZ1pVfVlTiERvpqVbr9Rj++8dzuPs7r+DeR09bvl6v8Z0nnsP+Nx8EJ+wGAHDCHPZffwN+8OMXHX+ufj84H6ib6nqEFcdm5kuPUPuEC0pxVOIRp5+5AGadDuENIhKPSJx4U4MU7VUqEqI3Ouo84YvYCjzcSp/QdQOlQg6csANXvGEf3n3zzzuW10sxNkZJsaMPuya0zAFJUdKRcrCJEDALPCQOsVQbT337GZyZmXX08SnqXRWdNoNGlUNiVHHcXyvyLBIpsnZBUvmbXQ21CvEUOxmDuBqsG1LsdDQURWSBv3E9qlYvzFbwxKkSDAPI17v46jOz0HTv3/BSPI1QOApN2QwA0JTjCIWj2HnhFsefK2LLu21UuHWdd6vpBqYKTdfzL88Gq7gja5aujCuOZxRTUDLEsAZRiocZ0+clZMGMhwpQ3q2i6aiW3FEbAXNDmDbrgl3ypda7Km6989PQlAg274zif338zxzL66UYHyf34kqJ9eVeMwhW6YrDyRMUEYlHp30MzVoU9979CccfH+hPniDFHc7/f1CVvxCgqudCSYemsIgm1KFSvFyEHBx4sKNPtSqtP8VR0XQ8cqJ/u56tdvDUVMnz11JpKaiVC9h60bsAxsDr3nUdaqW8K2pjWOTAMgyiSQ01M99wPSqOiqbjn358Bv/85DTuOXIK0yV3jlTPBWqfKM4Tz5kYMhCV3NmIRiUeLEeai4aDducvwrZJ+FY3GBtaqjYCwLgLSjG1T9QrPKp13ZUoukZfzq3qivJGPbv1cnA2NG7lSwNkePHWa7ahnP0RgC341r/cC4ZhHBtepPBi7cbMIcQgkeJclnyNJp1fu5Vi3ZBit2BXitdjtNeLs1U0Ootf8+OnSuh6HHNUaSm4/dBd2LjtrUimVdz00Y/hw394t6PDKhQMQ46zokkV9TIHXV+fQ5I/OJHHbJn4rbuqjn9/ds6X/w/LU5wVkNroXvIEAOuiF9Ss0yG8QVjke0UCAXkP0IziUFhDPOqCXS/Uyyou5lhX7jf19oLiDheIVTzCIRQxP79BIcU0X9rh4g6ADC++5eD7wHLzADaAF+O45dZbHRtepKjZ2+xcWjt6AlIuOv7QqwYt8Iml3FH5V4LznhSHbcbz9XiDfma6PPDv24qGF+eqnr4W2ohWtEd6uZA8QREWSYqBrjFo1dnAXJyXi0pTwbNnKn1/1+xqOPKK90Y9SynOCBgZdy95ArBZXxJkQ7PePnNDOAOamaprDAqlYCjF7a6ORpWkYrhxc45KXC/FwKWs4mpbRSVHrruJURUxhxMMADOBgrbaBeTz2ytdcX7tJiYmkEjEoWunAQBqNw05HHVseJGiZrdPjLpEik3rS7XEeS6cLYWCObA5MupsvvRqcN6T4qjEg+NIJmW9vL6OcrO1Ngr17pL//twShNktVE2PXGm+14jmdIyeHUQpNjc05fU3JPnkVAn6gOPTF2arqHk4LNlRNbQVDYbRX7ri1oYmJJBWu2iSTByruru1qUMEE+EATsL31EZnm8QoopJga7UTXPEVL6x4jriQ+xoWOeukJ1D2iQrZ0MgurF21mMfe15Ih8suu/QXMzDk7vAhQlV9AOKYhEWNdIYiJCI9QWEM9IFXPiqajXKBRej6/GAxJsaVa0WP49USsTmTrZ/33fL2LTNXZKLSl0Oio6Ko6NA0o522NaLJ7RyFRibT+AAhcAcC5oGj6kkq+pht4ZoGC7CYqTbJWtRIHtcvaijvcWzurKtb0g7cD4ikdwjsIXG8SvhiQFIO2QtIwIjENsgtzLBGJ65Fil5RiOqwViWtIRFlX7Gs02qteCc4cTq2pod1wxz4BAJ+/70s4+Ms3AwCufOOv4M8++wXHn6PeUVChGcUundTRtWtUOHQCsHZ0QBIANoz7/GIwJMXWLpoMbAVn17scTOYa5/yeox5ZKOjxe7XAQ9cYz+wT/Urx+lm7k/nGWY+uXpiteDbV3csopnFs7irFACw/eKfFotth0BwmUJyXGBkhX4My9NOfYOACKRZ5hKMGxJDuWqtdvaOgkieV0m7FW4XNjOlGgOxPtF0vkdIhuLARiEo9P3g5587a1Uw/eNKFODaKsI0UB2FD0+4S1ToU0RCP+F+dcd6TYlkgrXb0KFfRjMD4bM6GekdFrnbuAPHjmborE84LUW4uIFYu+1IBM2M6uT7zbo9nzq7yN7saTubP/j1OoecnJmvlZpsdhX1D06isr7UbwjmMmcelNBvYb7QUDY0a65qnmObdJkZpq53zNimLWLkU6QWQZjQ6JBmUzy5tIqQbLacRlXiEwgZCEY2QYodVfsMw0Oho5obGHT8xQDhPNECk2O2N6Epx3pNi2mpnP8oNys73bJgqLC+6q95RMVdx30KxkFi52YhGEZbIUAXDGKiX+XWj8mu6gVOFc6v8L83XPHg1/ckTAFk7hvHWD74ePnNDOA/qIQzKKV29qaHT5BBLaAgJ7tweIxKPhEtZxRaxypE8ZLcqc0kboQpNZVAoBsMPXjCbCNOj7rwenmMhi5zVSOj02tU7KrpdA7US79qQHdAfhdhW/BcAW7YBSTe84CvFeU+KAapaqWjWOGgq1sVR7pkV5Nkux2axVlRaZOCPKsXJcfftEzTvlgxJBkexOBdmy61lnUaczJ3dYuEUqMpfnBcgxzSEwgYiIu/qFLBdKa6Vg+NLHMJbpFMsWM5Ao8qiE4AbdDZHj+ANVzK6ARLLlhyjVc/OE6tOB6hXTGLlklIs8iwSI2S96O/Mb5SKhM7QHF43EJWI37ecFxwfhq53VFQL7mYUA/2e4iCIEXRAMjpUioODiMRZx/BBeaOcCzOl1rK/d9KDY3iLWGUExFIqRMlASOAg8u69xex5t7V1NCQ5VVzehkbVDZzMu7+hKdvi2NIeDEgCxFsZW6d+8CGcQ1gKlr/xleMkvDUkujfoGpU40mpX4FFtOk+KKbFyK+eWgjaj0YxZP9FWNNTK5F6zYdy9e04sRDYblRyPmtMbGnu+9Kh71hfaJNntsCjX/P/MtRT3SldWgyEphpm5uI68qfWOah15LweFendF378alK1hLVukl8vEKizyYBggFGnh+JOvIJtxPiLHDZxZJikGgFdy7m5oNN2wBkZKGR6pje5H6QGEDPWSQ9bHRnQI59F/lOv/e+Br9/8bAODZH/6ja88REXkkxxToGoNKiXV0M19v98of3PSlAsCY2WqXz/nvB2/bmgg3jLn3eiIi8fvWShyaLd3RKMm+fOkx95RinmORSJkqf9Z/lb+j6LYoveGgXSDQf4MO/lHuXHn5KjHF6WV4WFeLtqJZpKaYETDiQfIEAHAsg5DAoVY+inYzjH+/567AD0l2VR2Z6rkHJClOFRquplBUWwp0w4BhmErxRveH7ABycxFDBnhRJ6R4HViWhnAeIcEkxT4rxbIsg2EYPPPIUwCAH3/vfldqfAGz1S7tTopBdWGbnYuf3HGZ7wAAIABJREFUY2pTKAZgSJIOa0myjkTMPbWRWF8UGAaDatFZX3GtrfQpxW5uaNJp8vX3Pnon5uf9FZPKNQ3dNkuUYhdiEFeKISlGv1K8Ho5yVzM4d2qZg3mrAVWhdR0o5XhbcYe7xEqWZfzam3ahMPsjAOM4cvh+SALnyo3MKcxX2gMLO5ZCR9Exu4pN0HJBFf56mYPSYT2J0gPIRpRhgFhyffnBh3AWYZG3lGI/SfHk5CRuueUWsDxpKBOlJm677TbHa3yB/mivSoG3So+cQK2tWI1oCRdjvQBgwwbytVxgPIuPXAptRe8Na7lIrKIS8YMDZEPjJCmmpStSWEMsTuyHbmHUHEY8+VIGv//7v+/a8ywH1H4TSejDQbugwO4pXg9HufOrKOSYLjWhu3ThKjXJkF2txEFTWCuOzc3kCYDcyH7qHe8FyxUBJMGLcbzvpg+4ciNzCrOVlRNcN33FdO0K8/0ZxW5vaKgiQFNfgv6ZG8IdUH9jw+dmtImJCYSjMehqEgDQ7cwiHo87XuMLmMSKkuI87+jAVs20T4TCGmIxuEqsUnEOoqQHwvpCh7XcjvWyrx0hxc6uXSXnbvIEQMSkQ7/yBvNPadx9992unYosBzkzXzqW1Fx9vy4XQ1IMolaEIjo43jAn4YN7lGsYxrLyiReio+jI1NyJZluYUZzaSNVGd4nVxMQE4rEYdG0OAKB245AjzvfRO4m5VZDi0yvwIK8UtM2uZJLitEeeYp5jIQksaZJcJ8OtQzgPmXqKaxyaHX/fA/PzGWzacS0EScXP/qdbXTtWpm2OLGs4Hu1Va5MBPjc9qRRhkUMkqaERgFY7ki/tgVIcIr9bAKjkna3ppvYJN5MnAFNMets15p9GEQ6HXTsVWQ5ocQ+1dPiNISkGsU8wDEhtZcCPcouN7qp9s8vNNl4pyqbaWFxQ3OH2ETwA1MoFXPyaiwEAV7z+5zA/n3H9OdeClfiJKfK1jit1sEBPKV6cL+3+wAO1LdXLHFR9fZTmDOEsqFJs6AwKJX+P4P/fPX+PTTt+CtGkgf/9J3+OBx54wJXnCQkcJJFBfMT5vFtaE+y22gj0/OB1n1V+AOgoGhoVFpGEu1m3pMBDhyTrjirFqqaj2SXFHW6WrgBETEqN8ABUMOwE2u22a6ciy0HJzJcedSlfeqUYkmIQfyMAxJJq4O0T2bOoxNVCFnf91gdRLQ7OyFluFNhKUWz0kicA2DzF7pPiP//rL+LAbe8HAFz9jl/Dx+++x/XnXC0qTWXV7y23NjQlUykuzAuIJFRIsgFJYCHx7h9j0arYepmDYayP0pwhnIXIs4gnyWYol/X3tZAEAxaRmLtqI2AWeIwRD6nTxKqcF1xPngB6ySHNKus7KaaxXm41EVKQmFEGiVHi3XbKD17vqFBVoFp0XykGgFo5B0FsYN91N+OXfuVDvg3bKZqOSpl81sZcTA1ZCYakGIDAsRB51lKtgnxzPpt14qH7PoOTzz+Bh+799MB/n6+0oWrOq3Elm1IciWuQZAMiz3pimg+LPGIp0w9eCrY3NbsG+8pU0Xlfsarp1g25lBEwYtpevNjMAL1jZFVh0Wmyvh/BDuEPUiNEIcoXAjCsZfpS3fY2Rl1otau1VagKme1IuFjcQdE3JNn195Sn2iBNhImk7mrpENDzFZfzPKoORZ1WWypqRR6Gznii8n/qc/dhZGMIQBof/9O/cO1U5Fxom6khDGNgPB0MUux/KFxAQKuec9OidZTrZvHEajGIFN95cB/Ubu/vjxy+H0cO3w9elPB/Dz9r/b2qG5irtLF1JOzY66l3VOvYuzAnYGTCm0EtCrJu5KYS9Izps6n858KZovMJFKWmAhqEUcwI2LSDvD63veAU8qK1C66Xfwj3kDajvQp5f2+K1Jc6uklBWJRcfS6aQPHyk2E0uio03VgzmaN+YkNnzOIO0aFXOxiyQJTZRpVD28G83tWAturRDZaboCr/8SfDaHQ06LoBdo1rV20rKGVplJ6CqOTcPXoQZNG+dv5taKjCL0d1RMPBoKPBY30+IWJWPddNKT+oimO+vphYfeyeh/GqN70bvPAaACkIUgj7r78BH/vCNxd970rqoZeDUqNr/XdhXkB6I/mzF35igKgVYsiAIAU/73YtSnG9o6IwYO3XAuoF13XiKR7Z6J0XHOiPQmxUgr2hGcI90Lzbks95t1S1Csc9UIpDhBR3mhxaddaRrOJqW7GKOwixctlTLJJs2XaDQ63h72c3bw1ruU+KY7Smu8hDUQ1Hmu3sa5cad1/llwUOkbhuqvz+rV27q/e84AFIngCGpNiCbCrF3Q6LTotBM4DkqtlVBxIHpbsZLz3xaajKjwDMQuncjlA4ivjI2KLvnXZYcSyapFjTyBH86CZvleKImXfbs74Ed1hrNakhdpxZQbX3ckDXzorS83DIDiAqvz0K0W9f4hD+YMM4ITSVEuuKvWu5qDUIwUuk3D+CjyzKKl77MTyJYzMTgFwu7gAAiecQo37wvL/Wl2KefE0vvuU5jqgkIDmqwtAZ1IrOWChqbRWlLFm7pAfJIbIQjCbJthqsimdgSIotrIes4nytu+jvVAX43O9uQrcdx859f43teysAPoOZE7sGPsZ8tQ3FwRtP0VQby1keusb01EaXM4opZIEDyzBW3m1Qj+CbXRWNJSKnivM8jhxO4PjTMs7W67GSeujlwPKCL8oo9krlJ/5NAKiV+aFSfJ4ilWDBCTqaNX+PcvNF8jWZcp/gLc67Xft1q9KyK8XuEyugZ1fwmxTTU4bxUfdPGyISZ8WylfPObGjo2oXCGrESuFx3LJvX3maV9ZXr2POlg5BRDAw9xRZkgUc0SY636wG9Qecbi5XGR7+ewPxpCb/0e3nsfd0boXar+PPfTKJS+F/otE5BkvsvVppuYK7cxgVpZzxL1D5Byx/SE94ewTMMA1lkEUsSP10QNzPA4A0NALzwaAT3/MEE1C7Zn17zzgpu+mgG7IDt6nSpBcMwwDDOXPhpaohFij2qeKYIL2iSDLL15ScRDMO8A8AnAXAA/sYwjD/y43WQFAMy5Nbqaq4f+y+FvOlL9SIvdaFS7EQCRa2toJyTEIp4Q6wAIDVCvuYLrj/VkuiqOqplcsEcH3f/+WIhHslRsl6VHI9qywH7REtBOScgOa4iLHJr9iifCxLPIhonQ86lin8b0baioVnlsGV3B2HRXQ/8cjFUik1EpN7QT1CPcov1fmLVbTN46N40dl7RxKWvJekEvGjgxg9nUM4J+M4/pwY+zrSDvuKC+ZoKc+QN7bV9AgDkdZB3mxvgBy7nedz78Qls3NbFnX99Ctd/oIjHvp7At788eN3aijbwcVYDwzBQbHRQLWTxtb/7VwDwrOKZIixxEEQDobA2LPDwGAzDcAA+DeCdAC4FcAvDMJf68VpkkYMc6eC5I09janrGj5cAoEfsvCDFUYlHPN1rtXMi2qtqHsEnx7whVkDPD14s+OcHb5lecADYMOo+pYlKgrWhcUIp1nUDjY6GcpY3ByTdv/4yDINEyn+V/8z0LCoFDbxYG3qKgwaaPgEEN8Wg2Ognxc89EkW9zOPAbQXYxcMdl7Vx6TV1PPKVJJTu4ovVdNkZb2pb0axSicKcAI43kDAv9F7ZJwAgYq5dvcwHNu920JDcN76YhqoCP/+xWWzc1sW7fjGPS67O42ufj2H6RGXg4ziVQlFtqVA0Aw/d9xmUMgIEqQxRMsCzjGferv6qZ24YyeYtrgZwwjCMScMwugD+AcB7/HghssCh1TiFZpXDn/3xH/rxEgAARfMIftQTXyoPKWQgEieFDWv1peq6gbpZ8ewVsQJspLjoydMNRMdMMAiFNcQi7l+7oiEe4ZgOQdJRyfOorHHtah0VumGgnOORGldc94JTjJhDidnBtQae4G8++ZcwjBBmjn/PkwjX5WBIik2ERR7RhP0oN3g36MICUvzEw3GkNijYuW8xUXrDz5RRL/N46tuxRf82X3HGV2wn6YU5ASMbFLAcwLEMIh6+wcnAlgpNZdBusIEckly4ocnPCvjRN+K47mAF6QnyehkGkKO/D8Ngcd8f1QY+jlMq/4aRGO44sAdHDt8P4EIonaO448Ae/NZPX+6YPeNc4BfkgwdxI/oTjM0Aztj+PG3+naeQZRkXbYyjWngBwCjuv+dzxBIly16/FJQ99KVyLANZ4BAfVU2leH0Sq3HbkKRxtoEIF0GV4kjCm2GtiMiBY0mecDm39g1NtaWg22FQr9ANjTf3TnoiUvAhH1yWZTAMg2888BAA4PRLD0MWeV8+9wsxJMUmwiIHMWRAknXUy8HzpjY6ap+lo1LgcOypMK56a3Wg/3T3q5oY39rBYw8mFv2bphuYr6w+HoyiUF9Aim0ZxV4RK6Dfm1oL6JDkwg3N9/+Vg6HruOptJwCQrOk7DuzBk9/6FIDPIjP1etxx4A248+C+vp+bKbeg62u/iP3L957E/jcfhCCFAGwHw57B/utvwGe+cmTNj70S9FrteLQCOiT5E4pBH9C+NxbDMB9iGOYJhmGeyOXckZMmJydx400fAMuWAIxCCsm47bbbcPLkSVeebym0FQ0105dK0zDcRkTiEYk3cPzpaczMzq2JVFJi1fCYWKXiHHhRR73CouOTba2t6FaCgRdqI8MwZNhuVEUlL6DeITnTq0WlRdrxAJANjUcq/6iP1pfJyUnceuut4AWyD+f5qi+f+0EYkmIT9MNEs4qDplotVBqf/KYOQ2ew68qpgd/PMMBVb63h5AsyivOLVQMn8ortmcmFeQGjHg/ZUditL/VS8IYka22lz+es68Bj35ABPIRHv/bnAEjWdI+k/iUAAZt2/MmirOmOoq+pBISCCacQCkehdDQA22DoxxAKR3HBlk1rfuyVwMoHr3BQNMPRZJQhzoppAFttf94CYNb+DYZhfNYwjKsMw7hqbMwdT8HExASSyQR0PQsghU5bQTwex8aNG115vqXQUUj6hSDpSMW9IZRRiUet+DS67RS+/oW71pR3aydWJKPYu/SfSFxDs+afGEELILzMuo1KPJJjJDHCMLAmtbjSUjBzog4AEKS8Z4Om9ETEj3zwiYkJxONxqEocAKCqc7587gdhSIpNSDwHgWMC62+k8VkUjxwuAZjC09/5kyV/5lVvqgIAnhxgoZh2IPOWkuJmjUWrxlnJE14O2QFkQxNL9ZrRgrZ29g3NnQf34bff8SF0W2MA7sGRw/fjjgN78Ac//1aEwlGo3Q54cQrAN1GYeweiyQFZ045saLqolQvYf/2HAfDYfeUoaqW8p15woJcP3ihz0HUEbkPzE4zHAexmGGY7wzAigJsBfMWPF1LI5bB97zYAHA6890OYn5/3/DVYR/AeFHcA5Pj4Z/ZvQWbquwA24Mjhf0JCFld9fFxt2RvR3K8JppBFM++24l+rXavby7r1KtYrSgs8Cjx0DWvyFVdaCo4cfgQA8NwPPufZ2o2NsmAYA+WSP0OSmUwGe/a/GwDwU2+/zpfP/SAMSbENNMWgVgneUW6pST505Jh9L4rzewA8iB/+OyFVC4/ZASA9oeLCS1t45vvu+IrzVvJEf6SX18QqsiDaK2jEyk6KP3bPwxjf+lEAHQCH+9oHa+UCrj14Cz76yS/jov1H0WmN4eTzi2+Sa1X5Nd1AsdHF7YfuwqvfcgcA4O0/99O4/dBdnm9oqMqv6wxadTaQqS8/iTAMQwXwYQDfAHAUwJcNw3jBj9fyL//yAN743gMAgPf8/O/igQce8Pw1tBXN0yP4yclJvO2GG8HxGQAAL27Hu37mplUfH1fMSC+AkGKvPsdBaEZrq4SUR5O6d6TYjNTTNQb1MofyKkmxLMt4175NOPEMOaR58tt3Y9toxBNvbSRE6pVrFc6XxKZ/+qd/xiXXvB8A8Bu/c4cvn/tBGJJiG3opBuQoN0jRXjQP+GP3PIyLXnUHgASAB89a6QwAe19bx8yJEP7iN/8LqsWeL5DmFa8WlZZiERiaUTy6ibxGP4iVVQJRCp4f3K7yx9PjqJVeA+B74EUVardjtQ/efugu3PiRQ9i882LcfujtECQdT3938YZmttxek4et2OhaP5+f7Y/S89r6Ive12gXP+vKTDMMwvmYYxkWGYew0DOP/+PlaRsyhn7xP8VAtOyn2gFhNTEwgmYhDU08DANTuGEQ5surj40pLQTnrbXEHAIQEUovdrPp3Qlepaui2WSSS3t2vSVZxL5at3BycQ38uTE5O4qq33ACGvRDAPASJxc233OqJtzYkkJruZpX1ReWnmxmGNTCa9rfi3Y4hKbZBNlMM6FFukI7hKbGKp8fRbuwHAHDCD/tI1SDsNfOLp17agofu/XTfv63lGN5eWVyYXaAU+0CsOA7WMV7QSiBoSQYAFDM8WvUtuPDSDD76yS/j2oO3oFbKL/oZSTZw6dUNPPP9KPQFb8OuqmOusnr7S7bW2wwVZgWIIR2xFHkSr1X+sMgjkuxZX4LaSDiEu0ibzWj5vD83R1oiEPaIFANAtVTAlW+8AgBw0f4PIJPJrPqxymYjWjSpQhANz47gw6K9LtgfESln5kuPjHi3oYpKQq/VLies2j6RGh2HGIrA0DcDzBmo3Q5SyYQn3lpZIGJSo+JPL4Nle4lpnm3iloPgvJIAgKYY0KPcVldDwmOSMAiabvS15hRz2yFHp/Hrf/JXePRrX+pTgO248+A+qN0OgEkA78KRw+/BkcP3gxcl/N/Dz2Kq2MS1q3xNfcRqTkA0oSIUJhclP4gVENwhyZLNPvHyExEAwAfu+ClsuKCLGz9yaMmfu+KNNTzz/RhOviAvit2bKjaxJbW6VkL7hiY/KyC9SQHDACzDIObxxck+JNkIaGnOEO5jbMy/oR+AJBjUqxyiCR0hwRut6Iv3fxmf/Y8zePq7wMVXfQC3/NLqYqI7qoZWVyONaGMqJIFEHXqBEB20q7NoLlFj7zZoMMqIB6UrFNFQf013pbU6ganSUlArFyBHL8OWXQwu2HGbZ95aWeQQjmmoFHi0u95vaNqqbkXpBSWjGBgqxX3oK/Ao8YFRraotBboZ16PrgKa8Gle8PorNOy/GjR85hNsP3TXw52iiAcs+COCt4MVkn9UiU+2smoRkqjZSPE+IFeAPseJYhlycE8FrRuuqulVwAgCTz8uIpVSMbz33cdvFr26C5QwcfTyCaiGLu37rg9YG6Exx9Sp/ttpPiqntJSJ504Jlhyz07BNBLc0Zwn2Mm6S4XGJ8ybtttDS06yziKd2zOMlYiIcc1SFKOso5YdW+1Io5b1Iyizu8vP5yLIN4kiQh5fL+KMUFUyn2onSFIirxiCQ0iJKOUkZApams6n1banbxC797F3RtAhPbZfz673zcM2+tlRxS9WdIstXVUPfQsrRcDEmxDWQS3qx6rgQnxcDuSc2cFtGqc9i+99x+4Hh6HKFwFLr+bwDCULvX9lktdMNYdQpFP7ESkTatE9EQ7zmxAmiBh5kcEiBitTA15OTzMrbvbWE5991QRMeOy1p48bEIHrrvMzj5/BOWBWa+sroNja4bVlW0ri2I0vPhVCQskiNrAIHMBx/CGyTjLHiBKEd+5N3mCwYMg0Eq5R0hDwkcRJ5BaiNJjmh1tVV9pimZpsUdXh9Fp0b8rQsuWaUr3j1nVOLBssQyWJgXoC44zV0uSg0FrTqLTos1o/S8W7uQzT7hx3W3bStdGZLigIIWCQDkKDcoN2i7gnDqKJlKvXDv8shsrVzA6356I3hBw6Ydv7HIv3q60Fj562l2LUWv22FQzvIYM5XPuMdDdhSyrepZ1Q10fIoHWohys7d2lQKHYkbA9mWuHQBMPv97mD8l4cjhH8AwDCvC7bffdfmqPOGFRtcaIK0UeGgK69uQHUCsL/1+8GCs2xDeIiyZN+iqPxaarEnoUh76UgFCrlLjKooZ8tmzXy+Wi1Kji1aDRafJeRrHRtEbkvT0aQEQa2HFjBTzqnQFIAp5WOQwslFBKUN+3wsFkOWg3OyinDMHJMdVz5oIAUDiWUTiOrodFuWaT6SYRumJwaGiwXklAUBQo70qtgvlzAkJoYhmEZlz4fZDd+Gm//w/sf2yNgy8ZZHV4nRh5cRqztaGl58RYBgMxreYpNgnD3bYVPkbVQ6aBl88UoNgv1CefIFsaLZftnxS/Gt//LMAAJYnfkN72shq1s7eZJg3ByTTm/zb0IQEFizDIJJQA6fyD+EdZIFDmEZ7+UCKC+aAX9pDtREAoiEBIxuUHilurYJY9SVPKIh5vLmlzWgFH/zgVG0EgLExb+lMLCQgtaG3oVkVKW4pKGXJz6fGVE+FCYZhkBwh98msDxuaVpecDIXj+lApDirocQIA1Mp8YFQr+4dt5hUJm3d2lnX8bsfuK1uYm5RQL/e/+SotZVFb3rlgJ1bZaRLpNb7VP7URWKDyVzg0A5JAYY/qOfWCDEHSsXnn8hvpdlwegyRnoKsHwItSX9rIyfzKVX57asWiODYfNjQMw0AWWaLyV4KzER3CW4QEDlHqb/QhxaBQJF9HPRzWAohSPLJRQavGod1gUWqsXCkuN7s9YjXuXUYxBY3TKhU8fVoAvSg9OaohJntLrKISj/RGBa06h1adXRUpLja6FqlObfDWPgEAqRT5mst5b30plHToGoPo0D4RXITNaK9wzD+fzSDQuBddA+YmJWxaAami2HUlURVPPLM4FHyl5Gq63CNWuTOUWFGl2Cf7hGBT+QNEruzHodPHyYaGW8GviGGASOIxsNzb8OFP/GNfhFutrfYlSSwHs7a1y88I4AXdmqL2a0MjizxC4SamXp5HJhOMVqMhvIVsesv9uu6WTFJMB/68QixEfMAAUMzyqyRWipUVn57wnliNj/eGJL1Gq9trIvSaWEVDPFIbzLXLCH3Rm8tBra2gq+oozgkQJBKL6aV9AgDS5iawUPCeFGdMIh5P6eC54FDR4LySAEAWODBMsKK9dJuBPz8roNthsWUVpHjrRW1IYQ3Hn14c47USUtxWNBTqvefPzYhIjimQZPIG9yvCzp4cEqRjeNpEqOvAzCshbNm98sKUd3/oKuhaCJpyxaK0kclcfdmP0+io1usBgMyUiLEtCljzXuLXhiYscCjMPwWlE8Phv/sU1DU2LQ6x/hDiSZFAo8Z6fkKn6QaqZXIr3LjBW2IXkXiMbCDX99K8sOJTu0ZHRVvRUJwTIEo6oknvidVoigXLGaiWvW9G66iab7Fe8RBvDZgX5gQUGyu7L9NTgcK8gPRGEovp9YbGsr74kA9OPeheDrcuB0NSbANrRntFk1pgigSq7V4c28wrEgBg086VEyuOA3Ze3sKJAaR4ptRa9nDLmWIT9uSZ7BnB8hMD/nuKATPFIADWl7bSmybPzwjotFhs2bXyDc3OfU0wjIHjTy1eu8kVbGgW1kNnz4hWNBzDwHMvIkBqTt9/1VZkTj8CII0jh78Mgec8qTkdIjiQzRKIZo1Dy+O8W+pL5QUdyZi3xCoW4q3So2JWQLnZXVG0F1WWC/MCUj4RK1n0rxmt1dXRqJnDWrzX9gnBUopLGR6NzsrSQwomiS7MCxjZqEDkWc9qqikoKS4WPX1a8pw0Ss9jH/+5MCTFC0C9qX61vCyEvSln5pUQON7AhgtWVym5+1VN5GZElLL9F03dMHAiuzzFccqWj1vJZzF9XEVirAqATOR6nVFMIS9QioOg8tvX7szxEACsSimOxIkPeZDKn6m2UW0v79juTLFnnVC6DArzgkWKoxIPzocovcnJSbzl4PvA8mUALHhxM953082e1JwOERyEeKL2GTqDXMFbtdGqeE5oCEseD2tJxPbFizpKGR6KZqDaXr4YU6iTz29xnqiWfhAr2ax6btQ4tD2+7rZtSrHXCQaxEI9wTIcU1ixfcL6+fNGjUO/CMIDinID0hOK5FxwANlj54N5TwR4pHirFgQZttauXyQXK6+OghegnxRI2buuAX6Wgt5v6igeQq+PZ2rIew261+PfP3wtdjyE/8xAAcpHwKvh+IcIiCcJnWcO0T/iv8pcXpIbwoo4N21a3odl1ZROnjobQbff/fg0DOJ5Z3tqdsq1dfkaAoTPWBssvhX9iYgKJeBy6OgsAULtxyJGoJzWnQwQHLMsgYR6j5gYXdLoGGg0VjuuQvFYbQzwYBhgZV1E0fcGFFRCrYoMQK6o2+kGsaKsdEZI83tCYVcGxhI9rt9G+dsu/vhcaHTSqJKPYDy84AETDHEJhDRUfSnMoEffax38uOEKKGYZ5B8MwLzMMc4JhmP/uxGP6Bdm0T9BoL7+9qXZiNfvK0kN2O8YiuPnqrXjjnjHwSyh+Gy/sIpJQByqOU4XWOe0i2WobtbaKOw/uwx0H9uCJ/3gRAHDyhb/HHQf24Fevv2S5/1uOIyxyYFkgYhZ4BEkprhayeOzBYxjb0gS3yuv27iub0BTWinWz46X5c5PibLXd16yXPUNTQ2gcm3915pViHpe9bh8AYO/rbh4O252nSNMSCI9TDNoKSb2Ixr33pYZFHjxLCjyo2rgSX3Gh0UXmdBGdJodIvOwLsbKsLz7E6ZWqGpQOi0TKe/EqKvJgGaYvUq+wTF+xYRjI17sozJHrMNnQ+GBfoyp/1dvSnI6qoV5hwbIG0iPB0mbX/GoYhuEAfBrAOwFcCuAWhmEuXevj+gXSjEbIQ7Pqf7QXPRqvlTjUSvzAOK+JRAgH923CRELG/gtSOLB3sMrGssCuK1o48bSMhZtC3TBwdK561tdyLEMsFrQ+muMvAwDw4insv/4G3PvQ4yv933MMEk/ybqnKHwRSTOPYHrrvM2jVL4Da/fGS37vURoZix+UtsJwxcEOTrXaQrZ3dlnF8gT0mM0UuxmObyWv0a0ASAP7fPffjp3/xgwCA/W++HX909z2+vZYh/EPaGvrx9nnbCs1L9ScaKhriMbJBsWxt+RWojfl6Bw9+4asAgFNH/8VHYkW8vV7sUXNyAAAgAElEQVSTYnqqkPI4Sg8gpxsRiUM0UcX8aQWVQm7ZaUDVlmolTwBAeqM/SnFIYBFJ6J7bRdtdkkkejmkIe7wRPRecoOhXAzhhGMakYRhdAP8A4D0OPK4vkEUOkSTNKvY/xYCqjZTELPQTMwxw/SXjfX7QPRtj2DEWGfh4u65oopwXrOIGO56drix5hGIYBl6aJ6SZ1kdr6m4ATajdEwiFo9i+bfOK//+cAs27jSVU1APiBz+4/0LccWAPjhz+OoBNyE0fxh0H9uDOg/v6vu8NF43iN968Cx94zVZEpMEXCEk2sO3i9kBSDADPTVeWfB2GYeDlBWpy9oyI1IZeaohfyRNAf8Z0UDY0Q3gPOnBTKnn7vC2FpF5E4prnflyAttopaFR4dFrMsn2pIVnGr71pF579wTEAwMtP3IMDezd6PqRqt094fb/Mm02EIx43EVLEQjwyUw/D0KP42ue/gHx9eYOSuToRMQrz5Lrrq/UlZvrBPbS+tBT/UkPOBSdI8WYAZ2x/njb/rg8Mw3yIYZgnGIZ5Iue1aWwFWBjt5fcNmpJiety9kBRvH41gPBZa9HPX7RodWPCx64qlfcXlpoJXloj4OlVoomYbAKmVC0iMvhUbtnVw3Q03o1bK+6o2AuQoMmIlh/hPrP7wH75DFHVhPwCA449ZbXQUV2xN4NXbRsCyDDYlZdxwxaYlB952XdnE9HEJrfrij+1L87UlNwJniq0+bzoAZGzJE4C/SrEsEMWAYY1AZUwP4S1GR82hH4+b0ZodknoRT+q+DJvGQgJGNpJra9GMZdP0cxOr7z3+PPa/+SBYbg8AgBfn8K6fucnzIVWOZRBPkiKGQtlbGwONEvMjwUCWZdx89TacevFLAIDHH3oGH75+N2R5sHBhR7ZGkydERJMqJNnwxcIWovaJirdRiG2FEHG/TmfOBidI8aCryKJPtGEYnzUM4yrDMK4aGxtz4GndgSxwiCV7qpWf0V5tRUPH3L1lpkSIIR2J0X47x+WbEwN/djQqYfvoYrV4fKuC+Ig6sMQDAH74SgH6gAvy4yf7M1tuP3QXNPUSbLuYtbJz/SbFlh+8zEHTDV/VYlXTwcdGiKKuXAQA0NSnrTY6AJAEFtfu7L+aTyRk7NsyeE13v6oJQ2fwyrOL166r6vjx6cES24+n+tdO10npSmBIsciB5YBIjPjBg6DyD+E90sle3q3iYVZ1rqDD0BkkfVQbaflRflaAphvL86ZGUgiFo9C1rQDy0JQ8UsmEL0OqKeoHz3r7OyyahSHjPlCKyclJvPWGG8ELpwEAHL8X+6+/AQ899uw5fzZTJUpxcZ5HeoIIFn4oxbLZ4kuaJL277lpKcVz35XTmbHCCFE8D2Gr78xYAsw48ri+QRQ6RBM279Ve1qtrUPZopa1d/IxKHC9ODbRIAcMWW5KK/YxiiFp94JrzIVwwQP9uTU/3k6qX5KmZsTWgA8TjXyzwmtvcu3n4OawHUD66h3eSgdBlfrS/VtgrDIIr6xIXvhRhScO3Bn7La6ACyPoMuCNdsT0PkF380L7y4DUHScWxAXjEAPDVV6quVBkiu9Kl8fz5xOcej22GxwSTFPMv44mejCAkcOJYZVj2f5yDXXjL046UYkfO5RCAW4jG2mVzrczPkRDBbPTcpzlTbqJULSI69Dhu2cbj24C0oF/w5hR0xPb05D5vRDMNAyTxV2OBDgsHExASSiThU5RgAFZq6g4ggocGihh0Zc30Lc4JVAOKnfaLd5FBteqsUN6tmE+FPoH3icQC7GYbZzjCMCOBmAF9x4HF9QVgk2YNMAKK97Pmz2TPiIuvE7vEY2LMc921Lhwd+0HZd2UStxFs+5YV45EQBz5n+4tOFBr55NLvoe+ZOkZ+lpFgSWN/f3HJfgQeHpo+KI93Q3H7oLkQSr8fGC1W8/zd7bXQMA1y2hMovixwu3RRf9Pe8aGD73taSvmJFM/DVZ+eszUC9o+KhFzOLvm/uJCmBoWsXlwXfovQowqaX3++N6BD+QTZv0M0q66lqRXlk2qe81KhE4iQjCRX5GSIsUCXxbMhU27j90F3ghYuxcRuPGz9yCA888IDbL3cg0mnyu/OyGa2jkgExhjEwOupPgkG1mMd1N9yE1Hgb6Ym3oFbKY6589rUrNbpodTVoKlDOkii9sMj5UnXMsYyV3JHNeXc60+z2ssGDphSveWtiGIbKMMyHAXwDAAfgbw3DeGHNr8wn0GivaML/G3TFrHfutBiUsgLGt/YPU+0ci5715xmGwSUTcfxogfVh95VE9T3xjIyNA3JzdcPAw0cz+OZLmYFqMgDMTZrtetv9Ty+gCIs8ogny/+b3hsbu4Z0/LWLva/ub5zYn5bP+zq7YksTTU+VFf3/R/iYO/80YqgUO8fTi92a+1sEXHz2FLakwporNgWr5rLl2ExcGZ+1Cpm1p9hUpEBnTQ3gPWWQtpbjd9e4GXTAvj6NpfzaGNDFibLNiKcXz5yDFtbaCWluFpgLFjIB9r68jLHIQfCBWADBGk0M8jNNrdckRvBzVEQ35Q6zu+9I/4os/PI1SFijnrsTth+5CrtZBV9UHnvYBsE5dC3MCdJ3B2JauL6khFMkR8tXLUa9SRYemMgjHfjI9xTAM42uGYVxkGMZOwzD+jxOP6RdotFckoaFe8XcSnqqNuen+TFkAEHkWm1PnnjLevWExcR7ZqCA1rgwctrPjbEO0c6ckxFKqNZQYBGJFPcWA/9YXSorrZWIz2bit/zj0og2xs/78SETEpuTiAcqLXkWsEEtZKACg0dHw8nxtSfvI3EkJIxu7CEUI8QjC2oXNo/NahQtEac4Q3iNky0z1si64WCBkeGzcs6fsA01+GdvctUhxvtZF5yy/g7mK6UnNCNA1BuNb/SVWY3RIsuTdxsLeROjXKSU9iR3brCA/I0LXiag0V2kt+TOUFGfpfX2L4mv6j+UH9zAKkRLwRMqf4dazIVipyQGAFe1lHuX660tdOo5tS0pe1ptpPBZCMtx/sWQYYqE48UwY+iq5x+ykhIkLe0QvKQ+2YniJvqpnn72plBTPnya/l4WK/FKReXZcMrHYQrFpZwfhmLakhWI5mH1FshR+wL82OzvCpvWlVeOgqf6X5gzhPWi0V7Pq7XWXErnxtD+3Q4nnIAksRjcrqBZILJtuGGc9hp8pEWJFBZOxzV1fidVomgXDGKiW2WUlZziBtjWspSEk+Ld2IYHD2JYuuh0W1QJZg9OF5pI/M2X+m71Ayd8NDfla8NAPns3RKD3PnnLZGJLiAZBFHtGkSkhxAHyp2TMiWNbA6ETvSH7ryPJJ0SCbxa4rmmjWOMydXDmZ1TVC9iZ29EhxUNTGnqfY3+QQuqGxLny2Dc1YTFrWRXD3eAzsAq8vy5J2u+NPDh6UPBe6bQa5WaGvGXHhpskPhIReVnGj4u/nbgh/INO826p3G9q2QoY7Od7fZq1YSMDYll4CBQBMFZcmVmdKlFiR7/WbWEVDHMIx3dPP7tT0DE6/NA1Jbvp6BE8GJcna5abJGpwqNAZ+b67WsZpFs2dIHJsc1RH3YciOYtQckiwUvFNsqc1mJO2Pj/9sGJLiAQgLHASpjuJ8B6V81reIqGq79+FJTyjgxd4baEty+QHtg6LZdl1BfcUrVxzzswLULosJm9oYBGIVFjlIsgFe0P1X+U0/eG5GAC/2R+ltSy/vdy6LHLYMsMjsfhUpYMnNrPx3Pn9ahKEz2LTDrvIHYe14S+UnOdNDX/H5BqoU6xqDQskb+0xHMZu14hpk0b/bYXxAAsXpJUhxta2gYLbe5WZEhGMaInF/iZUskt9hs+ZdtNenP/HH6LbDKGWf9XVYKy4LGNvSv3aFendgXffxbK9EKTctYtzcCPm5oRkf8z4fnFqW/MiXPheGpHgAZJHD3KkfQNdjePCev/LlGL7ZVS1fZXZGtFQEgCQ9jMWkZT/W5qQMacHxEsfNghencPTxlV9Ip14mXtetu3vHe4kAkGJZ5MAwMKue/bNPtBXNujHkZ0SMbVbA2n79W1PL34gMslnsNn3Fx59c+YbmzDGydpt3krVjmGCo/PZ88IbPXv4h/AHHMoibsWj0eNVttGxH8H6qjXFZ6GUVW77iDipNZdH3TuZ6KmRuupc37iexCvFU5WddFyNkWQbDMPinez8PYBTZMz+CLPKeN/lRxEM84mkVoqRbdhYAeGmu2vd9C5tFs9MCxraS9fXT+hKPsRAl3VM/eNlMfR0bkuLgQ5ZlvGvfJpx5+WEAwKNf/xbSUcnzDxxVGg0DKMwKGN3UuzhujIdWFKHFsgy2jfSTq4fu+wzU7jfwyjMS9BVew6ZeDkEM6ZbHmWcZxHzMuaWQeI5k7pp5t36lGNij9HLTonW0BgAsQ5rrlosdA6wvo5sUpDYoOLZCX3G1kMU37n0M4XjXatCKSrwvUUALYc8Hr/lsWxrCP9C6Xlrf6zZaComA86vimSIeEkirWVrtOwE6lq0t+t5jfcSqd33xk1iFRBZiqInTR2dwZsbdmoLJyUnceuutEKVRACGwXBW33Xab501+FHFZAMsCo5u7yE731u6F2SpUWwnN6UITZXOT06iyaFR4Syn2M+OfDrjWPPKDK5qOatmM0vPJx382BO8V+YzJyUkcePeN4HgSh8ULW3DD+37W8w9czSRWtRKHbptF2k6KE4tTCc4FemR/58F9uOPAHhw5fD+Ab0FTI/jtd96KOw/uW/ZjTb0UwtaL2mDNe0gi7H/OLQUdtquX/VMb6YZG04D8nIDRzb21G49LS0b1DEJCFjAa7fd9MwxJoTjxdHhFG5qH7vsM6qUdEEMvWCUwybD/A5JAf716Y1jgcd4ibfob8x7l3bZpgoHPpDhhT6CwqY3Pz5C8eIp8vWOlF7QbZLBrLADEShY4lLLPo9uW8ck/+birzzUxMYF4PI5uhwgGujaPeDzuS5Mf0Dtp27Cti/nTvRPcekfFE2bLqKrp+P6JXrzD/CnyfRu3dSAJrK/vPdpq51VpDi3u8DNK72wYkuIFmJiYQCIeh6aS3a6qJBEKRz3/wFG1kQ5d0KM1gCjFKwUlxR+752Hsf/NBCFIIwLcBAJt2fAQf+8I3l/U4apfBzKSECy7uWSeCQqwAQopjtiFJYzXTaGsEXbsSjUuyWV9Ws6G5cIAnfPermmjVOUyfOLeNprcR+iqAS1HO/ivuOLAHdx7cFwg/MUAuzHJUBxuA0pwh/MOoWaBR9Gjoh8Z6hX1MMAB6hHZ8axeZM6I1RFtuKnjRdgz/iI1YWQVKF3Z9JVayLCMui8hN/whAGl/+4t+SFCcXT1czmQyuuf7nAABXvu4yzM/Pu/Zc5wJdu03bOyhlBLQavffRY5NFfPulLB54agb5Wm+OY5YWKO3o+m5fo612DY+qnoMQpXc2DEnxAJSLeex/89UAgN1XvgfZ7OJWMLdB1cb8LLnw2e0TG1ZBimMhAemoiHh6HKFwFGq3A14sA3gKtfI1iI8srzx+ZlKCprC4YE+PFKcC4CemsPJuyxx0Hb4cw1esfGlzQ2OzT0yshhQPqPLefWUTDGPg6OPnjnajGyFOeB0AFhz/NPZffwM+9oVvBmJAEiCbGZbFsNXuPEcv79ab52t1iWoVS+qQeH89xQBpmWzVOFTyPSvEd17O4ehcFT84nu/zE1O1cWJ7x1diRe0MLFcFEIYojbhuZ3jggQfw+oO/DgC4+Vc/6FuTH9BTiid2kOu8PdFJNww8faZsRehRzJ0kA5KJtOo7KaZKcdMjUkyGW/23LC2FISkegM/f9yXc+JvkA3fJ1Tfidz/5Oc9fA1UbC7MCWNZAarzXjx5ZpX+XDnjVygVce/AWfPSTX8bWi06iVtyNemV5b4XJ58juf/ulvQ95KkhKsVngoXZZdFqML+TKKl2ZoRmivQ3NRHzl6smmpLzIchFLadh2SRsvPnr2VkMA1kZIU8hGT1MfQSgcRXxkLDAqf0jgwLGM2SQ5HLQ7X5EeYcGyBiolb/yNhaIOXWeQSPpbFhMSOIg8a7VM2olVV9Xx4PPzePxUfzPp3CkRkqwjtcFfYkXtDLpGxKNuJ+KJnaFo/jr8HtYSeRZhkcOm7UQJpm2vZ8PcSQkTOzqBGHQOCSzCcd1TpbhZ9X+4dSkMSfEAhAUOobAOjjd8a7WjxCo/KyA5roA3PzcrSZ1YCJptfPuhu3DjRw5h886L8f7ffDUADkd/dG7FESDV0ONbun0Vw0FRGwGSMW1PMfAjlo1G6eVmRIQimuWVlUVuVSkdHMsMjGbbuS+DM8dCmD5RGfBT/aiVC0iM3oyxzRVcd8PbUSuRY9ggqfxkQ6OaQ5JDUnw+IixxCHt4lJvJkq9ByEtNyAImKLE6tTxitfFCQqz89BMDxM5w+TWXAgCufdvPe2JnKBUJfdkw7v88S0IWkBhVEY5pljViKeg6WTtKov1eOxqF2KqzqLfc3xzSeu7wUCleP+hFe6m+HeVSYpU5w6BVfxLVIulFHI+t/PidYktKxsJ5uM27OoiPqMtSHDUNmHxexq4r+/Mzg6QU2ws8amUODR+8qVWbfWJss2L9zjfEV7+huWBAWUth7tMAgH+9+9lz/vx/+h+fRrN2KS6+WseNHzmE2w/dBYYJnh+cKMXkM+eHH3wIf2E/yvXC+pS3SgRcf6pzIi4LCMd0JEcVzJ5DbTQMU200W0X9VhsfeOAB3PRLtwIA3vTuD7tuZ2grGuplFgxrYCwACQYJWQDDABM7Oudcu1JGQLfNWhsgv0UliWcRTWgwDAa5ggekWNHQqPnbRHg2BO8VBQDWUa5PVc9tRetlFE8xaNWfxkP3EgK0FqU4JHCLfp5lgUuvqeOlH4ehds++454+HkKnyWHXFT1SLAnsqu0cboDaJwD4sqGxr11upj+ObS0bGnuDIR2ce/q7fwrgOCaf22QNzi2FqZdDUDosdu3rrV08JASqd56uXb3MQTcMtBV/j7SH8B40HqruUdVzwZxbCwIppsR24/buOZXiapFDs8ZZBUp+k2IASKfJtaToQQmEVboS0xCW/Fcb6Qnglp0dzLwiQTuLFjP1MlnbzTuDsaFhGAYJMx98PuO+EFGqalA67NA+sd4gCz3Vymu1sdpWTOLzWqhKFMBxHDl8P+44sAeXbVveQNxS2DKgOOKy6xroNDm8/OOz596+9HgYDGNg575g+omBXiQbAF82NFQlVhWgnOX749jWsKEZjUqImpuP/gSRrwC4Hle84QNnTRA59hRZux2X29Yu4v+N1I6wqRS3mxzULjNstTsPQaqedTSrHDqq+59d6ksdH/V/c0jJ0aYdHWROi+h2ln5NUy+RDfYWs0DJb2IFAOPj5KsXySFNRbVKV4JwBJ+UyX1w26UtqF32rGrx6aMyBEnHph0dsAzju30C8DYfnD5HPGkEIiN/IYL3igKCkC3vtqPongx9UFRbKj52z8PY8+pfMP/mFQhSCFe95d04OTm5psce5E3ds7+BSELFj78VP+vPPvdIFBfubSGW6t2sgkaKafoEAF8GtuiAZDknwDAYjGzskeK1qPxAb+3sCSIc/wAACfXy9WdNEHnuSBTb97YQiffU16CtXci+oRlmFZ+XCIlEQWpUOLS67p8UlExVc2xtWoMjoPGI2y5uQdcYzJwlbvH0SzJYzsDmXSaxCgIpNpXiapmB7vL9sq3oqJuxXkE4gqdK8TYzqvTUi0sPVJ86SnL+OZ4UrrABOK1Lm556aidyE1niBEUqAD7+QfD/3RRQhG1DPwA8Va2qbQXx9DgM/UIAAMdPQe12kEzEMTExsabH3pxc7CvmeOBVb6rh+SMRZM8UcNdvfdDyMFPkZwXMToaw77p639+no8EiVmGBhygZkMLUm+qt2lgxo/SKGaLqjmwgpFjk2TWrOXYLhZUg8pf/DXJ0BnOnXrfkzxXmBMxNSrh8wdqNRIK2drYNTWXYanc+IkSLBGqs65siRdNRr5C0i7ER/2+F1Fu67RJCrO774wcWXYcppl4KYdOODkTJQDTEB8IGFY1wCIW9KYGgUXqRuB6II3g6sJwaV5EYVXDq6GCrnNJlMHMiZK2x335iirR5UlLwgBRblqWRISleV6DH8N2299FeNXPIrlIgw2+/8af/G9cevAWt6trfsYN8xQDwmrdVoSosvvSJYzj5/BOWh5nime+R13LZtf3EKnhqI3lL2we2vARtIizOk4sdJcXpiLjm1r+tNusLTRDZsutiXP+zIprVy5E9M/gC++wPyNoFnRTLIodYirz362UOjc7QPnG+QRaIT1RTWBTL7irFzS6pgw/Hg1EiEAsJRPUd0SDJWRTnNy26DgOArgFnjoUsVTIoCTIh3tzQeLChbSnkeYJSABEWeUimYn3hJW2cXkIpnj4uQVMZvPjox1Et5gIz6EzzwUse+MELpr1m1OcovaUwJMVLgE7CA97XzlJf6va970M0qeLCS3fjxo8cwl9+7j5HHn+Qr/hTd+wB8AOcfOH1MAzW8jDfeXAfNA04cjiJnfuaSE/0E5V0wIiVxHPgbUOS3tsnTKV4nuRLJ8bIn0eja7NOAOSIbtAx6dVvr4IXdHz3gdSif9N14NGvJ3DBxS2MbOxfu6CR4n7ryzCW7XyEwLFIpMwh45y7SlKH5qUmguFL5VgG//Vdl+OOA3vQaX0TwGv7rsMUmSkRnRaLCy4m8wFBURtlkSN5tzX34/RaXVs9t4+lK3ZQX/GOy1soZgTkZhavy7GnwgB0ZKbuwUP3fjowjaKpBAtO0FEuuU+KS+ZzjAXAxz8IQ1K8BMLCQn+jd6oVVYqLGcFSGgHnCOjm5OJd7MfueRg7LvsugB0AboUghazWs+ePRFHKCqiX/7++4zyeZQJzQbaDqvy1Co+mx2oj3dAUMwISYyo483rtlM1kkCc8ltJw1duqePyhOKrF/hvEy0+EkZsW8fr3lvv+PixyCIvBSQ0B+jOmh6125y+SKW+Gfqy62VgwfKkAcPdXH8H+Nx8Eyz8O4ALw4h7rOkxx7Ekiauy8nJLiYGxuQwKLSMybZrRCSYemMkik9EB4coGeYn/pNaR1cGH2/50H9+EbX3gFwGMAcjhy+H7s3zbiah32ciELLCJxHZUS62oUpqrpqFJSnA7Gui1EMK4EAUR/ioG3A1tV2xG8Xd1zStkblFccT49jwwUvAHgUwJ9C6YQRCkcRjo3hG19IQwpnkJn6RN9xXsoBS4AboGvXKHNQdcOTKXYKuqEpZYS+Ibt0ZO1KMdBvobDjzTeVYBgMvvLZ3sSQYQDf/IcRxEZUXPH6Wt/3B00lBsjReSjSK83xI2N6CP9B49FyLvsbLVKcCE401I5tWxEKR6GrhwEAavdtVvskxdHHI9hwQce6NwRFbaQZ014MSdJTBLqBCgJS5jU1PaFgwwUdvPhoPyn+z5/8LoBrwHIPAgAEKYSbPnCLq3XYywUt8GhUOXRU99aurZIovVBEQzQcjM/cQgxJ8RIg8VA9f6NXpFjRdLS6GnQdKGUFpGyDWjGHoltCAjfwOL9eyePKN/wbGHYUidHvIjcj47/d8DjmT0voNH8ZgNZ3nDcasCE7irDIIWYOSeo60Ox4s3YdVbMUkmKG71f5nVKKRwarCmObFbz1lgKe/FYcT347BgD4/r8mMfl8GG//YMFqRKRwws7hNMJmaU4k4U8++BDBQNokxUW3SbF5BB+O64GwTwBkRqNWLuC6G16D1HgdybFfttonAaDTYvDKczIuvqqXNx6UDS7NmG7UWNc9xTnzV5IeDQ4ptq/DJVc38MpzYTRrPYo1dWwbAEDX/g28KEHtdpBOJVyvw14OiPVFQ6PKunrdtQYkA+IFH4RgnZ8GCCGbfaJW5jw7hqdKY7XIQ1MZi1g5PdC2JSUjV+v0/d3th+4CADz+Hxl86RN7Ucl/DgAwsf1e5P9/9s48XI6qzP/fU1t3V2/3dt89eyAECAkh7JvsixAJIogBN5hRGR+RkRlXcHAZxmWcQZGIOvNzlAEBHVA0gEJAQI0gu+xb1pvcpW/ve3Utvz9OVXV3cveu6luV1Od5eMi96a5buadPnfe85/t+392bUKvS3e3KE8/CBR//vCMDK6CesVAVgnKBQammYG+1rfXkdOcJWSLIJetBsZ9nLWtwEvHz6BB5ZEq1vf7ujEvTePN5EXd8sw+P3B3D0BYfVhxfwHHn7d0G2mmuIQDtrMQQglBU1v3BvaB4f6RHT4raXfRTMh0MnKEpBoCYKJjPYU6Q8KffrMRln9sAgAZ/Lz5Rg1JjsPDgXQAEsIwzfG4BOn/DUQXVEotc0Wb5hBEUO6DpikFjULzmtDwe+78Ynn0kYkrXnvpdFL7ADhx5xgocf/7X8MLDv8TIyMhc3W4Tfo5FKKJgeLuAso3rZcWBkqU9ceZdOQBR4OALaBB8KorZ9i3QhiY1bVh66UfwVmcDxiu2Mzj6rDw+/9/bcMk1I/jH72/HkkPvhyxVzd2tcZzn2KBY4Jq72rVtQ6OPXYKjHsW99OfGLG6SMZGEghM0fPzGXTjjAymEIgrO/fAYPnL9bjDjzHInjh0hBAGBoUWSWRZlTz5hC4SQSwghrxBCVELIUXN9P3sSjxEQoiGXYWz1h09lqC410qE6wtIMaG6os/LEApQagxefCJvfe+iOAoBRvPXCtwDQIjunaGoJIYjocoaRUXvlE2bTFQf4Sxt0igIYXU44f1kV85fl8Jv/UjE2lMQbz4rY/loA513px8WfvgHzDjgY137l27a3w54ufp6hmWKbnUMMyZLoECu98fAyxRMg6qn9oN7Ao10LdL7BvQBAQ6bY2sDK0BVPpKnvnldD9zyaYXzkLuqJe9x5l+LJB+42i+26WmxGYRdUPkGz4IUM174NzR5j12luaKz9PS2IiXhp197ZXwDwBTScd8Xk586EODNTDNRbPY/t5lFTqB7c55Dq8n2IlwFcBOBHc30j4xHysxDDqrlAh2xqIz86Sv/f6ZQlAc0AACAASURBVCC/1LCfh8AxkGQVS1ZU0Luoisfu6cQvb14KpTYfwGsAvoMnH7gdTz5wO3jBhw9XK3N92yaG92zCxiJJTdOQSdGdfk+3MzYEAHUP6RB5pIp66+2u72Lwrevx/c9kAPQhPiDh+HfXn9tOcm7yC/TEpJRnbZUbUsmSH32LJAR45/z7G/GC4gkwj3I7FOTbeJRr+tyO6IGVmW209gNk6Ir3lFCMh3GcBwDvu/oGALrm2qbFqlUCeuMVQHcOaXOm2Bi7uBkUW5wpnkBXbJBLjuK2f7sWH77upnG73EX8vGMDzYDAIRRVUMzSz1ZZ8oJiq9E07TUAjiySBegC3ahvtOs5YwRusZgtl581saCA4WwFhADnfDCJ224cwFFnvoPX/ppFMVcC8J+mjO0rN35jrm+3CUPjmxiz77NVqdWbrsQd0HSlkXhIwN+fdjBkyVhXh5BP/TuAt8Fyl4ET7mp4rXOSSn6OBsWqSjCWUoH59vycsm6DKIYV+ByaKXbWJ8pBGEe54Q4ZxQy1mLG7dSXQ4DwxwiPcKUPw6VW2NtjuNHZImymttiy2k0aP6XYWSRqa4vQIB4bVEInTr63Wg4sCN2mR40N3/GDcBiwGjh47fUNTLTOQqsTTFe+HGAt00WZrL6Olbcxh7WYbM4iHv6uAY9+dxTOb+lEqHADg78AJWVPGtnzJwrm70XEwGjIkbQyKy7V60xXRYcVa8aAP1/9sE9actha8zw/gh+B93TjitM/hy7d/v+m1TipUFzgGoSiVvIyM2jcfskUF1TLjKMeXPXFmqs8hGNrU3e/4oGlAycajPAPjCD49zJnOE4TYY9C+oDOA57anZ/XenvD4bSydwJ5NIIrS1NlwKzA2NMlhHh3dNdOj2I7q8AUxEWMFqel7n1u7qiFDAWzeeCc2b7wTnODDtzf+zfx+j4ODYrHBCrHYxiz/vgYhZBOA8crar9M07b5pXuPjAD4OAAsXti/4CuhHuZkEh3Jt74JSq0jrnbWcpEsFmqVNhACXfmYEJ69L47f/fR26+nkcd94vTBmbU5wnDAw5Qzpl38+oNFjpOaVA0qA7LCAS74FfDDXV4QSCzbZ6Ascg6hArPQNjczhqo/QloQfc4agKgXNmTtYLiifBcDHIZ1kaFFdl+4NivdAuOcJjwTKqFQv5OPCs9R+g+Z0iGEKgzsKsuyfi3MAqwLNgOUAMK7rHdGnqN1mAWWg3wptFdnZVhy+MiXh+R3NDjut/tgm/+fG38NLmTahVK01OIY30RJy7oaGuL/T36DlQzB5N08604Bo/BvBjADjqqKPalk6lz90qdr3tQ0myTy9ruFs4SZcKjF8EO7BUwif+7Qbz6/ddfQM4hiDmkMYdBj16l7JsmoGsqOBsWLdKkt7iOeK8bGN3iD5b85nx63AMukLO8/g39OBjiSle2AIj+rWddjrTiBcUTwIt2FKg1BhUS4ztC7SqaihWFagKkBnlcfjJBQD2dSwSOAb9UT92Zcozfm+vgzPFhv9hqEO39mpDtlFWVFOmkRrmcfDRtKtRNGBPdfj8ThEsQ5qq88fLUOxp/A8AvQ7e0Ih7Sl+8TPF+h59nIIYVFPP2eVVXZQX5jDN1qdOVN8VCgmOcJwzCQRaBUN3FIGxDUFypKSjleHQvkOAXnJVtjYo8fDwzbh1OI06UsMUM6YuN/uDJpC4HdVBx654462ngMGhnNLoo59sQXBUkGaqmjeNRbN/En42uWBRYRB3Y3tnAx7HgGIJQ1LD2sj/bmK/I0DSgJhHkUnXpi11tsAWOQV90742JkaG45nu/wAlr1zcZ/wNA2M85rr1zI+IenSS9TLH1EELeSwgZBHA8gPsJIb+f63tqxK+f0NWqDDI5e8a/Iqm6NZQC0eesZVAUOAR9U2dAux1UqGXQqAe367nb3J7bWZliYHrSQifKD422yykb/cGN0xlDe+5EnLs6OgCRZxGMUt1mOwq2DOlEag+PYrsCKwBYFBfx5JaZbQ3HC8achtHqeWSHAFnVUKnZ+wA19MTpUTp2hvOE1UV2jSzpCmJXujnLP1WGoj86uXPFXOPnGzLFWRYlSZriHR4zRdO0XwH41Vzfx0T4eRahCC36sUvfWK7pR/AO7azVE/Zja7U46Wt6HSiDCgi0iKqYsc/vtlChYxfqcJ58AgD6In7sTE0u2XPiGhqP0ZOTTNq+oDil6/iNANyJOGuL7DACunwCgN7Aw95M8UQexdGAfYFVX8Q/42DR6YEVUA+KCxkapNqd5TedJ8yxs8d5opFF8Zln+Z34MG5EFFj4RBUcr+rSFy9TvD9iHK8aXsJWU5Jkmm2MKPA70PJvOgGvE+s6fBzd1BZz9gXFY0kVqkoQjqq21Nq0ylTPWIFjHOVRbCDqVojZNIE2izqjqZBkFfksHa9uhxW3NuK8T5SDaDzKzWc4W02tgXpQnNZ9bjt66Nd2ZooZhmDxDIOreZ3OD4qNsSvmWCgK7M/yNzhPALBdPgHQbNJMCz/ndTh77ESBAyGg0pcMi5LX1W6/JK4X4ozZpG90eqZ4Kt0/yxBHyicCuvNP0UbZ2qhRrNXlTF3qQMfkQXF/1O+4IjuAavmDele7Ss36joRlvUDSF1ARDTpvzhl4QfEkGI0EALSlYKsun+ARjlGPYkJgu3XL0u7QtF/LMQS9DiwS2JMAz4HjqTvDyPZMG7L89bFjWA1R3aPYbu31kq7gtF8rcIyj7dgA+mAmBGar55Kk2JK18HA28Tj9f8qmoNi09XJoprg/SjuOTkRvxGeLs0OrBHj6Oy3k7OuMNmY2XXHmc0EUuEmt8gYcmpjw62NXzNuT5S/XaMc8MaLA78CNqIHzZpWDCPAsOEGDX6QLtO2BVbWuKTakE0HBHju2RhZ3USeD6TDQEXDkw3hPAgKLt1/cCAB4+Of32H4M39i4o7OnBoalG4iwzRZ+B/RMf0Mz0OF3XLX6nhBC6MKqS18UVbMla+HhbLp0mzS79I3Fqq5LjSrwC857ngUEdtLAyqkSNqNIUpYYpLP2zNtEgn4m4g4+gp+s62grTbPsxM+zECP19upWYzRdcaKVXiPOexo4CNG09lLaVGhX1xQbRXbtMPj2ceyUrYMNFs5Cx9puAoEATl3egzefuxcA8OITz+CoxTEEAvYtJI2dCM2xE3nbj8kWdAambYK+0KEP4z0xrBALWTr/7N6MejiPHr0QJ59lbOlqZ+hSn3/sNqTtNGZtgfmTyNScGlixDEG0kwbDwzZ1RkvrpweGJ7ITWRgb/wRP4Bj0O7BAEqCndLyvgMRgDjsGd1l+/ZIko5SjEg0nuoYYeEHxJPg4BixDzKBYklVIsn1Zq3ylBlUB0qO8mSmOtKnrzbKe8LRetzg+/eP6uWLLli0478KLwfFZAADLzcdp51+ErVu32vLzFFVDQZfWpBoad7RjQ8OxDA7ont6YTPSgdho020Q9pgHYruX3cB6hQIPfrQ3JiNEEDdiSQ8/ja1/7muXXt4KJNrEsQxxdG2A0ZkjY4BxSU1Rk0zRs6e11pnwCoGPHs3sH7Uu7go49rQvwLEZ3/BWKHMFN3/qG5ddvlCx5mWKXYhzlUr9be10MSpKMmqIhm+SgKsQMrOws1GpkWW8I3BSTNeznHGk6vif9/f3oiEYh1wYBAIrcCT4QRF/feF1vW6egexRLVYJ8qi59savpyp4s74tM+Rq3jB1ANXmhDupTWy0Tc8Phsf/g5xmIEQUlG1wMAoEA/mHdhfpXCdx66630WW/jSdJsWBAbX9Y2fwanQ3OBoQcfG7M++CtJNLDifSo6Is4NrASOweJx6j2W9U5f7tZOAoEAusJ+7HrnjwB8+OXtv7B8ThhjJ3pBsbsxGngYWSu7jnL3dJ7o7G2ffAKgEooDp9CnLuudXjbZCaSTCZyw9lwQRsX8ZacimbDJ2wkNHsW6v3S7x25RTJzS7H8m2uO5Zq+udp58Yr/DLPrJWS9b27JlCw5afb7+1RhEUcTll19u20nSbPFx7Li2i9M91ZsrunStrx1FkpVagxbcwYEVABw2EG36OuhjsaTLmc/hLVu2YP369WA5eroqCPMsnxP5ooJKkWrOnajjN3DunTmEAK9be2VYqCpsK9jas3FHZ097AysAWDk/OunfH9Ln7IdxI3fe/X+4+NP/glBUxfwDT8XffWWDbT8rq4/d4FvUbN8nUo1iu8aOYQhWDEw+dge7aOz8fENXuyznZYr3QwKCfZ3R+vv7oYFGbhyfR6VSQSQSse0kqRUO7W8+BeIY4thso0GvHhSnUwSqaq3EoSS5o1gLoD7yjX7TRy6KTbugvd309/cjGo1CkUcAAJIUtnxOJPQWz+GoCp8DHV8MvKB4Cgy/W1UlKBcY2xboekc0PVOsexS3S1MMAPM7xQkN4fuifvQ4tEBgPAJNRZIcKjUFsmKPHtwYu7888BQA4G9//BEAoKONY7dqfnTCB248JDi2Wn08mls921/g6uE8Ag2ZYqvlE7KiIp+hS9+//8+PcNVVV2F4eNjSn2EVB3SHmiR0K+ZFHJ8hNTqj2eFiYHjdBqPOD4oJIThnRS/Cfg5Lu4M4YkHHXN/SpIyMjOCIk48EABx58vstnxNGI56OmLPdhLygeAoCjQ080pxtR7m5St3SKxSVIfg18CyZcXOGVjluaXzc769Z2NnW+2gVgWPo769DrrsY2JTlP+3Q+bj27OXY8lICgIRnH7kV1569HANdk2dvrSTs53FI//ja4sPnO/thvCdUPkHnQyHDepni/RAaFKso5ayXz5RrCg499mKwvIojj16BDRs24N5777X0Z1gFwxCcfnAPGEIQ9nMTPp+dhNHAw/AZt5KSRJ/nTm26sifxkA9/f/JSrFs9z7EFdgb33nsvLvvU3wMAjj3zCsvnREI3eely+EfYC4qnQBQ4hDvpQzmftq+BhyGfSI/y6Oxtf5bY4IDuEJbu4WbQG/HjIIcf2Y2H3yiS1PXgBZs2NN+/709Yc9paEOZAANvB+3w49qx1bdcoHn9AfK8CnGiAx4qBqQvxnERAYBHuNDai9jfN8XAeRmBVLTPIFWzKNkZUiD7nL4GL4kF89ITF+OBxiyAK7U2SzAYzy2+Dc4hRrBWMOrPpitvp7qL/t0MPnkzSTUGXg/2lAS8onhJRYBGJGUExh4JdmuKGQjtDTxzxtz8oBoCzD+1DV4g6J4T9HM5b2efItpRTYbgYmEWSNgVXbCgOvxiCpi4AITsgS1VEI+G2axRDPg5nHtJrdsJiCMFZh/a6otlKIwGeheCnTXPo6Ywnn9jf8HHUzxQARi229mps8exkbWMjUZF3vGzCICDQ5jvFLItSzdpnbraooFpybtMVtxOPERCiWd40R1ZUZFL0mt0OD4qdv+2cYwICi0hMz1qlWBSrVVt+Tq5cg6YB6QSHQ44tAGhvkV0jAYHF+mMWYlemjN6I3zUP4z0xtKmVEouaZI+1l6pqKFRk5DNJ8MLBOPjoDCKx9ShkbOpPOwXL+8LgWYJ3EkUc0h/G/E5nmvxPhpENC8doUCzJKio166vNZUV13YZhf4EQgg69je+oxb01ilUFxTyHUMQdR/BuI8CzCEUUDG8XUJasXS9H9YYg4Q73bGjcRDDAIhBSkdf7Mlhl/Veq0doewNlNVwAvKJ6SAM/CH1TB8SpyaXsq4Ss1BZKsopBhUasypkdxJDB3w8OxDBa5oFHHZNBjeHulL/mqDFXTcPnnN+ALF0Sx4KAazlx/A44/YO6EU0u7Q1ja7T65i4GfZ6iGslNGLlXP8lsdFOcq8qStdD3mlliM/j9peaZYRjHrw8BSCQHeWwKthjbfkWyRT4zoxVpGgxAPa/FzRoErg7KkWBYUG5KlQFhBOOjszYyXJpkCUWBBiJ61StHdU1W2dqLX9cTNPrdzJZ/YVxAbsvy5FGdLUNyoBQfa71G8L0IIQUBgENEzxYA9RZJ53TXEw5l0mfpGBppmXRBk6lId3m7WrRh68GKeRaFi7bwdG6P/j8W8oNgOjLEr5ayVvhhWemEX+Et7QfEUGMdrNGtlzwJdb/5g2LG1t8XzvkqTHjzF2qIHNzyKk8N0rGJeUGwJAb3ANa9niu04ocmVvQI+J2MExfksg0rNOhunfFlBKe8OWy834tf14JpKMJq01n7LKABzerGWW/HzDMSw9f7gpmtIh+z4OecFxVPg41jwLEGkszFrZXHxgL44G9lGUz7hZYpbQhQ4hPWgOJfiULAhM2huaIbpZyPW5wXFVhDgqfSlUmIhVYjlnSRrimq5h6qHtfR2U+0htfaybvyTKQ2aSqiDAe8tgVbDsQyiuhetoQG2gqqsIJemAVVPt7N1qW4lYFMnybJEG6CFXGCl5z0RpoGfZxGO1bNWRktmqzACq9QIB7+oIBCiAnenf3icjtGNkBAN+TSHog0uBka2MTXCg+NVhDsVcAyB6I1dSzRKX/JpDgWr51y5ZumRvIf1hEUWYpgW6Fi5QI8m6Lh3xjRXuuq4AUMPnrCwSLJUpcEaIRq6HV6s5VYCvC59sbhpjtmJ0AWnM15QPA1EgUMkJqOYY6HIsDxrNa5Hsd8rAGkVUWDBskAwqiCXZE0XAyupt+emY8cwVPbiLbat0VgkmUtZ38DDkL14OJeAwEAMV/HC489gx67dll3XCNRiDu+s5Wa6uujGw/CmtQLqYMBCDKsI+Z0dWLkVH083orUqg1TWurWyUKE65ZAXFO8bBH20mYCmERSyrC1ZK4B2s/P0xNZhZNojMdmUvlgdXNWz/LynJ7YQkWcRbswUWz5unp7Y6fh5FlJlO4pZDt/79jcsu25SL9aKd1l2SY89MLxos2mCmmLN5qMsyfUCSe8kzhZopliXviSs2zSOJVWoKsHzj/0UmdSoZde1Ay8ongZ+vrFgi0PepgW6OVPsBVat0uh3axRJWrmhkRXVDNZSw1zdNWQOrfT2FUSBQ6SzoUjSpo2ohzMJBAI4bF4HcqmXAfTgl7f/hLqSBAItXbcqK8hl6LLX5R3B24bRGa2YZVGyqMC5WFVcU6zlVgSOQSSqN82xUPoyPKKfHAw9h2/9243WXdgGvKB4GogNbWdzFusbS5IMSVZRLjIoF9iGTLEXWLUKyxD4eIZmim1wMchVZGgaUC0TFLOcmSn2NjStY3TFIkRDLs2hKMlQVes0wDnPjs3RbNmyBe+9+P1gmCSAHvj8AVx++eUtt043dKkA0ONlim2jI8JC8Km0YMsia69SQ3tuLyi2j07dYt+wv2uVQCCAz6//gP7VKG699VZLNrh24QXF06DJxSDJolC1bkHNNkgnAHiBlcUEBQ7hTgW5NAdVtTYoro+d7hrSZzRd8cauVRr14PkUC00DChZq+T1NsbPp7+9HRzQKVR0GEEO1UkMkEmm5dbqhSxX8KjqjXmBlFwGebmoLGdYyC9OSJKOYZRGKyl4Ruo3E9KA4aVFT1nfeeQdLDjtP/yoBURQt2eDahRcUT4PGTHE+TSuhFYuyVoZ7wa63SwAAwU/PLMJeUGwJAV36oioEpTxjaZa/scgOqNuxeRua1jHcO8INDTysHDsvKHY+qbEElq5cBIDBu87/BIaHh1u+ZlmSUchwCHlH8LYSEBqtvayZt8Wqnil2QQMIN9OlB8XpFLHEoaezqxcEPQAAjs+hUqlYssG1Cy8ongaiwIIXNARCCvJpPWtl0QJtLM5PPvhXAMALj/8QgCefsArRV9eD51Ic8jZk+VN7ZPnDnnNIyxiZIDFUwtsvbkculbAsy1+pKaha2AzCwx5+/etf4V0XngsAOP2SL+Dee+9t+ZrFKs1ehjq8wMpOTGuvrHV+t2MpBapKEIw438HAzfToWnurmuaUJBmFHF0Tv/XTH+Oqq66yZINrF97qPQ0au9rl9YKtfLWGqNh6RvDkgwcgSVUA3wZQwTObfoRnNv0QX/L7US6XW77+/g71u5UA0CJJO7KNqWEenEA9inmWIOjzplWrGE1zcqmXUC0vxkO334i1x91iybW9Ijv3EO+mi3LCooL1oiSjkPGjo9s7grcTP09lDolB3rJMsRFHRWPUx9/DHkIiA7+ot3qWWp8nJUnBQUdcgNyYgsOPWIlrLzvToju1B++TNQ0MF4NIrN5Rx6oGHrf85s9Yc9paEGYpgB3gfT4cd/Y6x+pt3EaAb+xqx1pqxZVpCIpjPTII8WQvVhEIBHD1GQchMfgXAH3YvPFOHLU4bklxhiedcA+Gi0E2zVriMV6qKshnOIQ6vGyjnQQEFqEOKlWxSlM8NkYzmPEur+mOnQR4FqKFXe2KuhY82OH8bnaAFxRPC5FnacATk5EdI7jlnz6IbTt3WXJtJhiDXwxBU+eDkJ2QpSqiUefqbdwGzRTr1l5pztIGHk2NO/o81xAr2bJlC044Zx0YNglAACf045Tz3mvJZjHjBcWuoYdKEVHIWLNAF6qyLp/wNMV2Qtu0K5Aq1jSBkGQVqTHdSq/bC4rtxC9Qr+JijrWkUZlhpeeGxh2AFxRPC4Yh1Ku4U0EmAWx9+RncetO3Wr6uomooVGTkM0nwvuVYcfyBOGHtehTSFnmheCDoY+ELaBD8KnJJ6xp4FKvUSg+g7hNxr8jOUvr7+xEOh6EqgwAAWeoE5w9aslnMlryg2C10xRkwrKa7GLQ+b8eSKlSF0EyxC7JWbiXAswh30GDYCvloSaKbGQDo6239eh4TE+BZBMOGfMKC0xlJRiHL0aDYBXPOC4qnyafPXoHH7/0GVCUATRPx27t+1rLXXq5cg6pp+OAXN6BW7cD8A4N439U34D9/fLuFd75/EzClLzLyuvTFCk2pcQRfKREUc2xD4w4vKLaKQiaJFcevAACsOP4yJC0SlnryCfcQ9DMIRankwYoFemSUHsFHOlX4PF2qbTAMQUyXOYxaMG0L1XpX0t4er+mKnTQVSVogfSlWFRQz9JpepngfYsNv/oxFhxhZqj4IPn/LXnvGMW56lE72Ts+9wHKCprWXbHa1s0IPnint4VHs+UtbzrduvQ1r/+5yAMDqU67Eh7/8fUtaxnryCffg5w1tqjVHuYlRGqjF4ioI8YIrO+nqonM1k2RQlVsLrkoSdQ0JRhQERecHVm4mIOia4jxjyZwr6e253aLjbykoJoRcQgh5hRCiEkKOsuqmnMi8gQEEwxUAAMstRE2qtuy1lylRVwQjsDJbPHvZRsswjmsiMcV0DrGim1mj8wQAxPSx8zY01hFo1IOnrClwNSRLueQorlr/HkdbA3nQrFXIbALR2thXagoy+ueoq8fTpdpNb69u7ZVuvdiuWJWRz7AIdcoQXRBYuRm/Lp+ollhki60HxaNJFYpMEIoq8O8H8omXAVwE4AkL7sXRiAILSdoOAHj3R7+FE9aux67dQy1dc89McazHyxRbjWHtFe6UkbMosAKAbJluaPZq3OFtaCwjKHDwB1VwvGoenbYqfTEkSw/d8QO8+PRT+NrXvmbFrXrYhChwDUFx64FVMUufAb3dVtydx2T06PkiKzY0JUlBIc15WvA2YMgnAGukL8Y1glF3bGhaCoo1TXtN07Q3rLoZJyMKHD583WcBABy/CO+7+gb81213tnTNbMMRPGE0RLtkMIQgJHhBsZUEBA6RmIxKiYVUIZZoitOleuMO3qci1KGAY4gp1/BoHVEwXF8UZJPWZPn74xFce/ZybN54JzRNxa233tpybYCHfQR4WrVeyLYeWBWrCvJ6sVaPp0u1nYjI0oZXFkhfqKaYRdhzDbEdgWMQ6dD9wcdaO1FRVA1J3TegI6aBY52v2HX+HToEUWARjNCsVXbMmgXalE+M8ojGZbAcEPJzYBjvgW0l1Jat3qbbCvmEoSlODfOI9dZ0j2LO0ylaiNHqORqXzaC41Sz/fX98HmtOWwve56c/QxRbrg3wsA/D77ZaYpHOt5gp1ls8i2EFoYAXWNmNKFAHCupV3OqGhurKw51eprgddMZoMJxMAqo6+8C40TWkyyX+0lMGxYSQTYSQl8f5b91MfhAh5OOEkGcIIc8kEonZ3/EcYWStol2yGRS3UsWuqhqyZfqgSI1wXpGdjYgC29TAo1hVWirYKkuK6XWcGuZN6YTXuMNajKY5Hd0ychbMOQAQwnH4xRBq1YVgmJ+gXF7Qcm2Ah33QoJjOtZGR1q5V9DyK24pRJJlPty59SecUVEruKdZyO3G9aU4hw6LUgq9/SVJM6VuPS6z0pgyKNU07U9O0w8b5776Z/CBN036sadpRmqYd1d3tPkGX0bo3GpeRsWCBzuraRoBmijt79CI7L7CynKDAIdpFf7/mMXwLY5fWM/yaBiSHeMT7PT2xHfh5BgwhNFM8xkHTWg+K0yUJ+UwSy4/8B6jqFbjkko94xXYOptHvNjmGljaz9cYdXraxHYgCzezSoLi1TPGwviEKd8rmZtnDPnr1pjn5DItSC2NnyF4A9/hLe/KJaWIe5XbVj3JbWaCNwEpRgGyCa7D08ia81YgCiw4jKE4Y0pfZT3Rj7Ep5BpUSa46dl+W3FkIIRIFFtEuGVGVQLjAWBMU1XHHDLThoDbV6+9GPvoh7773Xitv1sAGWIejUrb3yLR7DF6u0YC/sBcVtIcCzCHVS+UQrDZNkRcWYfrgc7lDg572wxW56eggIoyGfbm3sirq/tBhWEA66Y861asn2XkLIIIDjAdxPCPm9NbflPESBa5JPaBqQK7cSWNHFPTvGQVWJacfmHcFbj+jjEAipEHyqmeU39NyzoVFPDACxPi/Lbxeij0U0rm9oxjiUJWXWnqeyoiJfqbfmDkdUdHRYdqseNtGjHywWMmzLC3Qhw3lH8G1C1PXgpXxr1l7FhiP4rm7Nq9toA6EA9YTOp1qTvhSqMnJpFuFO90iWWnWf+JWmafM1TfNpmtarado5Vt2Y02AZAh9Hs1ayxKCUZ5Ar16BpsxOPp4s0KEsO0UAq3k+/jgS8bKPVBJv04PT33UrGMWWM3bAxdoZ8whs7qxEFYNthewAAIABJREFUFtFufQPZ4glNulSDMV1Twxz657feCMTDfgwtYqu2bNmi0URA9jLFbSAg1KUvrejBDdkLAHS75Aje7QR4FuEY7STZ2kaUWumFOxXztN3peOcQMyDoY+va1DEOsqrN+hg+VdojKPaKtWxDNPTgXTIyCeukL/VMsTd2diEKXFOmGKhbGc6UxtOB1AiP/nleUOwGOqMsBJ/acqZ4NEF3RKEOxdOltoEATzOEAJBKMmZx8kwpVOq6VEPr6mEvVMsv65niVjTFNZopjrlnI+oFxTOgcYHOGBnHWS7QRqY4NcyDYTR09Hgd0ezC8A7u6G7dOURVtSb5hBhWEAiqYAhB2OeNndUEBQ7ROF1MjbGbbZtmI8OvaUB6mEf/gtYq4t0KIeTfCSGvE0L+Rgj5FSHE0SISUe9q14q+sVJTkE7qLd87PflEO+BYBp1xuhHJp2e/oSlUZeQzHHwBFR0RL2RpB4aNaT7DteQxXazSTrJhF0mWvE/YDAgKLDq6m7NWmfLMtamVmoKSpFdUD/Po7K2BZekHkXeBubXbCJhFkjVkkxxUheqCZyN9yZRrUHTfxuRQ3Y4t6GM9f2kbEH0sOEFDKCo36MFbC4oLGRZSldmfM8UPAzhM07RVAN4E8MU5vp9JEQUWkTjtSFmY5clcrlJDTg+K4z0qWG+utoWeXvqsLKS5WY9docFKzy1H8G6n0U6vFW/4sbQCqcIgHHOPa4gXgc0A0Uc7oxGimUFxehYLdLJYD6STQ7wnnbAZH8dC4Bh0dMlQFYJChoWiarMqlEw1jF1q2LNjs5ugUJe+5JLGnJtdkaQx74zW3AP7aaZY07SHNE0zPvxPApg/l/czFX7dZzyX4lCozm5DVKjQ9wNAX687mgjsCxhFkvkWpC+FimzqUgO8OwIrt2PY6ckSg7Hk7JIHsqIiMUo3nxEXNV3xguIZEBRY2nWuQ6lnimexQCcLVfPPqSEeMa9Qy3YCPItotyF9mX1wZQTFqgKkRjkzU+w5T9hDkxViC3NO0zTzfalheh2v0A4AcCWAB+f6JibDOMrNpbhZZ60K1XpQ3NvnBcXtorODAe9T8MdfP4RtO3fN6hqGLpVqwd0RWLmdgFDXg48mYJ6OzoRGj+LQ/uI+sb8hNmStjAW6MXM4XYyMVaVEUMhyZrbRyxTbR9BX9yrOJOjveTZBsbGhySY5KDXGzPJ7/tL2EPTtPeeKVWXGRTuZUg01pS57AYD+eftupng6nUgJIdcBkAHcMcE1HNGFVOTpCV05zyKTV2clezIyxYGwgmjYm6vtIujjwLIJZMcIfvTdb8/qGnl97KJdnnyiXQR4uhEFgHxqdlr+fEU2rfRiXQoEzh3hpjvu0iEEfXs38MiV5RnvopKFZvcCL7Cyn6CPa3IOAWYZFBcnGDtPPmELZqY4LqOQ5SBL9DhuprriZLF+OpMcEhDqkBEMWXefTmOqTqSEkI8AWAvgcm2CKNMpXUgDAotIzDjlYWa1QNNMMb2O6JKMldsJBAI4e0UfKqU3AAxg490/AyEEgUBg2tdQVQ2pnIJynsWrT/0c+fTcbc72JwghiHfTx0JulkWSRYm6VwBAT697NPxeUDwDzExxQ6tnVdNmHFyN6dnGukexlym2m6DAIRhVwPKquaFJFWcWWCmqZp4MmHZs5th5Gxo78PMsOIY0tOmmD9nGIHc6jBXqc3RsiEfXQGud8dwMIeRcAJ8HcIGmaaW5vp+pMArtAMxaQpGryMinaMbZyza2hy1btuDcdReDMKMABiD4/Lj88suxdevWaV8jX22oJRh5Brf8x7dsuluPPTG09/lZFknmK9Q1hBDN1Ja7AS8ongEh/Si3o5se5VXLdPczEwlFoSqjbDhPDDX73HqaYvsQBRYMo29oEkZQPLPAKlWU6s4TwzwI0dDZ7XWzsxuxMcufnJ1saaxBx5/cXS+Q3E+5BUAYwMOEkBcIIT+c6xuajABf72o426A4X6khl+QQibmn4Mft9Pf3IxqNQFMHAQxAqlYRiUTQ19c37Wv0dkZw40c+pH+1C//z3z+ecbbZY3Z0d9FWz4U0a3YCnQn5Sg25FItgVEFYdM+c84LiGeDnGbAMQWcP/YAY2tTGBXcqmorsRnj4gwrEMC348QIr+7BCm9o4zqlhHtEuGZyggRAvU2wnQYFFp+7jnR6lc2SmQXEiT8dOlgiyY9x+HRRrmnagpmkLNE1brf931Vzf02QwDDE7mdGgeGZjp2ka8mUqnwh3uscaal8gkxzD4kN7AIRw7LlXYtfuoRm9/6EnX8SSFe/Rv9oNURRnnG32mB2hAG2JnkvPdiNKNcXhmHs8igEvKJ4RhBCIAmtmdo0q9mRh+gv0aL5R20gzVoQAAsfA76IPjtswguKOrrr0BagHS9OhaeyG684TQYED5/lL20bQx5kb0fSIsRGd/pyTZNVs1pIc5qBpBF0Ds7N185gberoBhtWQT83cN7UkKcjnCOQa48kn2sz/3vkLnHjB6QCAUy++Dj++7c4Zvd8f7YKm9QMAOD6JSqUy42yzx+wICAzCHTLNFM9CU5zTOxGGO9y1EfVW8hkS9HHo7NVbV47MPFM8kquYf270KPaK7OzFKJLs6JGRGeWh6m5cyRlkHBsD6ORuoUEL7o2dnQR9LAS/hlCHbG5Ec+XatLP8o/kKjFKy5LAAAPt1ptiNhPwcIp20wDk3w0xxvsGjOBL35BPtJOhrkL4kZ57lz5VrKGR9IEwNN/z3/8NVV12F4eFhO27VYw8CPPWGppnimT8vC3qm2G2SJS8oniFBvYEHy2nmUW62XENVnuYCnaOBlap6zR/aidEEItZbgyIT3PyP1yKXSmBsBpliIyiulAhyKQ7d87yxaweNY2fMOWD6m9GRXLOeGMB+XWjnRkSBRThOvYpzM2zzndO1jQC8THGbCfBsUz1AdoYNk/LVGhYd/G50dgMrVq7Ehg0bcO+999pxqx57IOpexYVZdLWr1BSUJQX5lPs6EXpB8QwJ6gVbnT01M2uladM7zi1LinmMm0txkGuMl21sE6LAgiHElDzseD2Lh27f0CSJmIx0UTIzk2O7abaxax4dc08Lbi+G9KWzVzZPZ4DpS19GG05nxoZ4CH4VoY5916N4X0TUbdlyKQ65GS7Q2XLNdDCIxt2lb3Q7hBD06i4GdOxmmimm7hORuOx1s2szosAiHFOQS7MoVRVI8vSbHeUqNZTyDOQag2iXbCY23IAXFM+Q+gJda1qgh7OVid5Sf03D4pwa2tOj2Aus7IQQgs+evxL/dd0Z+ncWY/PGO3H5cYumVcncOHZju+hYPXLXZ5FLJTzXEJsx5lyst4b0CGdKXxozwJMx1DA3k7t5xAeojt/DPYgCPaHLJ1lIsmo6+EyHXLnW0M1OBfEGv63EOln4RIVKX2aQ5dc0DblyDdkk62X45wDRxyEal6HUGBRzzIw2NLlyvaA92iV78ol9mZC5QMtm0Q/QnI2aiKFM2fxzQg+s4gPeEXy7uOneP2L1u1boXy0B7/NjzenvwdN/e23K9zYGxYldNFO86+378NDtG7wNjc0YevBYbw1yjUFBbx06mp96zpUk2TydAZp1/B7uwfAqLmQ5KDKaxnQqcpUa8ikOgk9FZ4d7Fud9BZFnEY0pyCW5GY1boSpDVjUzU2w8Bzzag9gofRmb2YYmX6khM0bXxQ6XdSL0guIZ0pi1yqc5SFWaddg9jUzxroageHRQAMtpiPV6meJ2MX/eAMSwD8BuEOZAyFIVfjEExd8x5Xt3penYfW7tKjz4040AdgEoYvPGO7GkO+T5ZtqIcfTW2acXuDbYsk1VbLe7Yc6pKvWX9pwn3AeVT9CxzqU4ZMrTH8NsiWqKw3EZIS+wajtBH/3d5/RM8XTbdGfLNVTLBJUSLdZzk4PBvkBAaAyK+RnJlrLlmpkp7uiWXSVZ8oLiGWLsVjv1YDYzWq+Gn6wVoqyoTRKLxKCArgEJjP5Z8TTF9hPycchnkgh3FjD/wPNwwtr1yKfHmo7Xx6MqK6bt3vU/24Rg5BgQ8g4AgPf5sf6yyzzfTBsx9eCGLZveTVDTMOXYDabrQXF2jIMsMV6RnQsxCpwBGhRnp9nmW1U15HT3Ce8Ifm4ICByiMRnZJIuaoqE4TelLtkH2Eol7Y9du/DyLeA8dq0xiZll+IygmRENvrwaGcY9kyQuKZ0ijfAJAk654MD1xx9TdmQpktb5DTgzy6J5PP2Q8S8wMtId9BH0crrjhFiw7og+FbBTvu/oGXHHDLU0Z/PEYTJeh6tmNSLwH1co8aNqb4AQfZKmKjmjU8820EUIIgj7W3IimRupzZfcUY7ezISge3UFlLz0LvUyx2xCFurVXdoxDZpoLdL4iQ1E1ZMc4ROOKl22cA4K69CWX4qBptGh5OmRKzQWS3ti1n95eDYTRkE1yyJRmcDpTriGToJZu4aC7xs0LimdIgGdpVztjgR6uB8U7khMHxdtTRfPPikIdDHoW0A9Z2JNOtAVjQxPvoxNW0RP7uXJt0l3wzlR9XMtFBrIUxaJDRFzzvV/gzIs+6PlmtoGgj4MvoCEYlZts2XakJp5zharcZLk3OkiD4t4FXlDsNkSBq5/OJaafKc6Wa9A0IDPGoaO75mUb5wBRoAVbssSgXGCmnXFMlyQzKPY0xXNDWKSB7Uw0xZqmIVuimeJotwzRRdIJAHBXCO8AjK520bgMhtWaMsXbkyVomjZudfOWRD0oTo/wUGSCbsPSy3MvaAuhBucQTSXIJDjE+2lkvD1ZxKr542uLt47Vx85wnjj9/adg3gFFnH7it3Deyn6b79zDdH3pkZs2oiO5CsrS+Obw2xrGDQBGdwrwBxXTjs3nsof1/gzLEMRiBIJPRXqUQ6qUn9b7UiUJxRwDWWLQ0S1DFPw236nHngR9NFMM0Cx/epoZx3SphmxSBABE4+7Spe4rBH0sOrpogJvV9eBTubfk9QLJ7BiHrnk1121mvEzxLAj7OTAs9SpONxzlFqryuBrHsUIVqYYjIyNjZWSKvSK79hBsyBQDzVn+rXsEUAbJQhWZhqyUMXZG446o5xrSFkINDhSN8glNA95JFMZ9z9ujzd8f3UlPZ4xnujd27iLkZ9HRU0NmlEdZUqbV0TBdkpBJ6FXw3V62cS4QBQ4d3TQoziQ4pKeR5afZRgnpBAdfQEU8RjwrvTnAaL6SGeNQU7RptXvOFPUTHfN0xl1JPy8ongVmcNVfMxs5GLw+nNvr9a/ubv7ezjfoka4/OATAW5zbhVHMaDTwSO4hfRlvkX1jpDkjNbaLByFa3UrP29C0hZCP/p5jfTWkhuttugHgzZG9s4YlSd5LWjE6yDdJJ7zW6u5CFDh6UtDgPjIV6aKETMKognffAr0vEPSx6OyhwVR6lEdqGp0oc2UZNUVDZpRDR0/Nq7mZI4I+DtEuGVl9DhkB72SkSxJ1DSnSgNptG1EvKJ4FxjF89/waErt4NDrMvDaUbwquJFnFK3sExS88sQVACn+673sAPI/iduHnWXAMQUePDIbRmjLFsqrhrZHmzKKqanttaIa3+xDrr4EX9MI7T/rSFowHa/c86lVs2P0AVFe8ZxHIy7tyUBoKWyslguwYb57OAN68cxtBgUVHj2w6/kwnKE41BcXuW6D3BQI8i464AobVkB6lrZ5lZfLuaMkiDZzTIzw6e2RzzfVoL6Juy1YpsaiUyLSkL+mSVG/c4UIrPS8ongVGxrF7noRKkUUhU3/QSrKKv25NmV8/uSVpBsmfW7sK1569HCPbGQBv4C/334lrz16OVYt62nr/+zMhPweWpbri5FBzUPTcjjTUhkDqjZH8Xj3fh7cL6FtUfzB4Wf72ENYzxd3z6e8+MVg/odE0NM25Sk3BczvSTe9P7CFZCvpY8Kz3+HMTQR+HWA/1h69JBGNTZBwrNQX5ioxMggfLaYh0qp4udQ4ghCAssujookWyqqZNKaEwgq90gkNnj1cgOVcEfRw6Ghp4pKYRFKeKEjINHsVuavEMeEHxrDCOcoxCOaM7ncFzO9J4/M0EHn8zgWe31xfn63+2CWtOWwvgYABvmB3VXn3zrXbd+n6PmeWfVzP1wQapomQGUyVJxp/eGmv6e7lGg6u+RXQxJsRzDmkXZqbYDIqbf++vDuXw6u4cqrKC3708vFcb4JEdno7f7QR9VFMMUG2q4R0+EUk9k5wZ5RDtqiEcYD1d6hwhCpyuB6fP36k2NIl8FVKFoJilkhlPPjE3iAKLqK4H/9mN38Hb2wanfE+yICGr6/ijXTJEl53OeJ+0WdAonwCAsV0Clh5WL7DTNOC57em93heJ94DjewAMgGHfhixVIYbCWLpwflvu26Nh7BZI2PJyFJoGNK6Tf3p7DLsyZSTy1b2asSQGBagKQd9iybwW6yJTcjcT0k9nIjEFgl/F6K7mDY2mAb9/ZRjkVWC8hlmJQQEMoyHe7xVIupWgj0NnDy2ITY9yUwZWhh3f2JCGUu4VSHkFwFK7b9NjHAxd8ZaXaefPRL6KQyYx7UkUJKRH61rwoM/rGDoXBHU7PQAY3lrE7T/4D3zsnDsmfH2lpqBQlc1McbTLfdIXL1M8C4yj3M7eGhhW2yvjOBljQxEAwLpPrMUJa9ejnE3aco8e42MEVz3zJUiVZm0qQAOqLYniXrIJgEonAJiZYi+wah8+joXAMSCEntCM7Rr/dz9RB9mRHQLi/TVw+tu8sXMfQb3QDgAyozxKkoJ8ZeJj+FE9KB7ZXkWl9Cp++9Ob23KfHntDx66GbIKDqtCgeCIUVaMFknpBZaxXRtCTT8wJA11RfOOKQ/Wv5uGJ3/wchBAEAuNvUoyNanaMgxhWIAZoLY+b8ILiWRDycyAEYFmgq7824QI9Hkec+gUAwGEn9OB9V9+AL3/3/9l1mx7jYMgdevRj+NHB6Y/d8HYfCKOhZ4GXbZwLTC3//FqTpng6DG/zoW9JfSH2iuzcB62Er4GQuj/8SG7i4Or8Ixbh2rMPRbUcA7ANv/vl/066oHvYB83yy1BVgmySw2i+Cm2CHexovkIDY6NA0nOfmDO2bNmCo844C0ASwHzwPj/WXXwptm7dOu7rjY1oJqHbsblw3LygeBawDDGF/93zJYzsnP4CPbzNB39QMX0bvcCqvdTlEzSwTcxk7LYL6BpodJ7wxq6dGEFx14CE1DAPeXoNliBVCBK7eQw0BMXevHMfQR8LjgfCMcV0lBjJ7e0LDwCyouLL//sIVhz3EVCV4Bb4/AFcfvnlEy7oHvYR8nGmHjw9yqFSU5r83xsZ1r3+0yM8CKMhGvc0xXNFf38/QqEwgB0gZDFkqQrOL6Kvr2/c1xsnAMlhHrE+d2b4vaB4lhi+qX2Lq0gM8vj+Zz6KXCox5fuGtgroX+w1EJgrDG/aaFyGL6DOSPoyvM1nSicAoEP0xq6dGHOud5EEVSXTzha/87ciNJWgo7s+P70NjfswJDSdPTVTb7o7Ux73tcO5CkKd3YC2BADAcIOQqlVEIpEJF3QP+wj6WMR6m5sm7c5OMHZ6UJwc5tHRJYPj4Tpd6r5EMZtCrE9GZ+9JOGHteuzePTzha0dyFWgaHeN4nzsz/F5QPEuMrFX/EgmqwmDrK0U8dPuGSd+jacDQNh/6FnsZq7nC0BQTomf5t08vsKpJBGO7ec+ObQ4xFsZ+PeM7vM03rfc9+ou/AgDeev5HAOhJj9e4w50EBVbvJGrIJyrjet4anUUzY7RN8JVf+Wdc9tErMTw88YLuYR8hH4d4nwxCNLPh1WB6/KB4Z5o23RnbzaNroAY/z3oFzXPIDd/7CVaddAByqSAu+tQNuPIrt4z7ukpNQaooIZ9mUasyiPXVXLmZcd8dO4Swn8Pn1q6CLB0I4GUAK7B5453YvPFOcIIP3974t73ek0lwKBfYpmPcDnFm2kiP1hAFDhxDIKsa+pdU8dpfg9N638gOAZpKvA3NHBJuKJJkWA1D2wQcMcnr6fysAvgugDye+8PNeO4P3wMv+PDp6vjH7h7OJujj0Nkr46XNtGCrBg27MxUsjItNr9umt20/+OiPYGibhuVHLsKHr/4+eiL+ubjt/Z6gjwMnaOjokU1/+J17dJwEaKFWsUrtFJNDPFaeWHBltnFfIuhjEeurQZYY5FMsSFxGtlzba/0bytazxADt+OtG1xAvUzxLwn4e1/9sE1afsgyABGCl6Tt8/W2PjPuenW/SB/L8g+o+t17Gqv0YwdXA0ioKGQ759NS6p8G3aFZy/jI6dgLHuK5Tj9sxvIU5Hoj3l7B548uTSpYMX3BCjgDwEnifD2tOfw9+uHFzm+7Yw2rCfg5dAzUoNQbf+8fPIpdKYMtYcyfKSk0xM8XJIR6dvTWwLLzgag4RBZrt7eqvYWw3ncf5iryXJvydUTqW5QKDYpaOddgbtzmFZvmp9CU5PPGGZnuy2PSaWF/NlR0kHfNpq9VqGBwcRKXijgwOr6g496AI3vXVz6Bafg3AOQCOgC8QhBgOA9j733HEGTVcenwWHT01ENBOP2+9+Uab73xu8Pv9mD9/Pnh+7rOrYT+PdKmGgaU0wN39jg/Lj9p7kjey800//EEFXQOe88RcEW7YQKrq31DKL8BDt1+Piz/9lXFfH4n3wBcIQ9NWgzA/hyxV4RdDWLLA8wV3K0Efh+759Nm6840KHrp9Awb6/xWnHNRtNuZ4a6Rgtvg2tI0MIXPaFc1t65sdHBeTsfK7WyFVCDp079vd295GqsGyi6nKOCGuQY4SPPjgawhGFYRY4LXXcnN123POXK+dQR+HmBEUD/FYsqKCbckiDpsXbXrdDj1QNjLFsV5PPtESg4ODCIfDWLx4sSu6DtVkFamShLHdO1CrDkBVAxBD26EoMroGFo77nsQgD1Uh6NV1qTzLIBbc9+UTmqYhmUxicHAQS5YsmevbacoUA8CuLdMLihccVDELJL0iu/ZTlyxVAXwJwI3YvPE3k0qWxoaCACI450PHIJ9aj1wqgag3dq7lzJULIFVjAHYDOBCbN/4Amzfeiat9PlQqFWiahhcHM+brk8M8Vp1UQNA3t93s3La+2UGqWEUywSA7xmPekgoYlp6WdgV9YBiCqlx3pCjlGSQZAb2LqugMs2YtyP6GE9bOkI9DrI9uYoyAd3uyBElWIXBUbDBWqJodJoe3yWC5MVRKowj6xo+FnIxj5BOVSgXxeNw1DwxGF/53DSxEqMMHVWERiQ9MGBBrGiBVGPD+elEIt58UDxBCEI/HHZMlMbyKgxEVHV017N4yecGWLBEMbfVhwUENWvDAvr+ZcRocy+DGO/+ANaetBcu9BgBg+WMnlSwdefrXAQCHn9yJ9119A6644RYvy+9i/vD0Szji1CMBFAAcZErWbrzrcWRLNTy7PW3aQhlH8PH+WtMpw1zgtvXNDhhCwPE0gy/X6O9B04BcpQZJVpsaJhl/z/EaWMdEKe3HCWtn0MeBFzRE4rIpjZBkFW8M583XvLQra/75nZeSUOQ38dDtG7xMcau46YHBMgQEgAZA0ANdqcIgENq7Ehqgk1xVCQS/1nSN/QUnjW0kUP/YDxxQxa53Jg+Kh7YJUGSCBcvqDyYvUzw3LJg/D34xBEV+FACg1I6AX9yNSKx73Ndvf53KXoyW7ADQ4QXFrmXpwgUIBEMA3gIhy01JDBPsxE/+3Ow/PLKDblx7F0qmnd9c4qRn4FzAMgScQNdHuVZfC6uyiqosNb1WlggYTgPD0GB6f2auPzdGYBvvl5DcXZ9HT21N4sCeEEqSjFd2ZRtO8QYBPI3NG+9EQLgTfr8f5fL4TiNOZD/eg+3N4OAg1q1bh2XLluGAAw7ANddcA0mik/WnP/0pPvWpTzW93sgW8z4NIBqq5Yl/ncbf+RoyxVYExaFQaK/vnXrqqfj973/f9L3vfve7+OQnPznja+2LGAVbALDokApGtvtQyk88dlteCpivNfCyjXNDxM8jn0nixPeciWi8iHj/JcinxyZ8/Y7X/Vh4UAWMPryEeGPnZkJ+DvlMEvGBKqJdJ+OEtesnHH+jLXvvQmm/PX5vZKbrm9XUM8UaatLka19NYsDzKpYOdJnrbCMsy2L16tVYsWIFDj/8cPznf/4nVHX8hNRknHDCCTN+DwBs27YNP//5z82vn3nmGXz605+e1bWcjsAx8PEMeubXMNrQ7CpfkfG/T27DXU/vRE3RcP3PNuHwky8BMA/A6xB8flc2y3F1UDw0NIRTTjnFEu9JTdNw0UUX4cILL8Rbb72FN998E4VCAdddd92E7zGCWoYBBJ8GqTLxRK+WGDCsBk6oZ4o1VWn5vsdj/fr1uOuuu5q+d9ddd2H9+vW2/Dy30di4YfGhdAe77bWJrWPe+ZuI+IBkdiEEgM79QAvuRMJ+DlfccAved/UNWLpKgSKvwRU3jO+bWS4w2L3Vh8UrKg3v58Htz+exLicosPj7r27AEacsQy4pYt1VN0w4/qM7BXCC6tqCn7le31pFluWmr1mGgGGoJKJWnXgOahr1hed9mvm+PQkEAnjhhRfwyiuv4OGHH8YDDzyAr371q9O+N0Wha+/mzbNzotkzKD7qqKNw8803z+pabiDs49C7UEIhy6GYq49dsapAkulmJBLvgaouAwAw3DuoSe5sluPq1eHrX/86/vSnP+FrX/tay9d69NFH4ff7ccUVVwCgO9GbbroJP/nJT1Aq0SKsnTt34txzz8Xy5cvx1a9+FSwhKBaLuPyS9+L9l6zGhRccjl/f80sAwIvPP4cLzzsLZ7/rBFz63vdgcMcI/KKKi9aejX/76r/gwvPOwre+8W9YvHixucMtlUpYsGABarUa3nnnHZx77rk48sgjcfLJJ+P1118HAGzduhXHH388jj76aHz5y18e999y8cUXY+PGjahWqbZu27Zt2L17N0466SQUCgWcccYZWLNmDVauXIn77rtvr/c/9thjWLt2rfn1pz71Kfz0pz8FADz77LMbK1CpAAAgAElEQVQ45ZRTcOSRR+Kcc87B0NAQAODmm2/GoYceilWrVuEDH/hAq8NhK2EfZx7JLVxeAcNo2PbK+P6lqgq883IAB66qH/8IHOPKRXZfoDHLu+jgCjIJHtnk+K4Cb78YgKYSLFtdL6Ls9GQvroYQgqCPM7saTtaRcmSHoHtaY841xbNhrtc3ACgWizj//PNx+OGH47DDDsPdd98NYOJ14NRTT8WXvvQlnHLKKbjxxhub1rdKuYw1hx4IMBK2vLMF6y+6AGe/6wSsO/cM04Vp+7ZtOP/MU/GhDx6DW27+FwBTyyd6enrw4x//GLfccgs0TYOiKPjsZz+Lo48+GqtWrcKPfkSb9jz22GM47bTTcNlll2HlypUA6qejl156KR544AHzmh/96Edxzz33YNu2bTj55JOxZs0arFmzxgyiv/CFL+CPf/wjVq9ejZtuuslcM1VVxeLFi5HJ1Is9DzzwQIyMjCCRSOB973sfjj76aBx99NH485//DAB4/PHHsXr1aqxevRpHHHEE8vm6VtcphPwcehbSUwVDljQe6VHqSPGR667C+e//sCub5bjvSQG6S2wUnt9666249dZbW9KuvPLKKzjyyCObvheJRLBw4UK8/fbbAIC//vWvePnllyGKIo4++micftY5ePOdrejr68d//eQ+JIcE+CIJ1Go1XPe5a/HTO3+Jrq5u3HP3/+GW71+Pm26hkzObzeI3D25Cd9iH559/Ho8//jhOO+00/Pa3v8U555wDnufx8Y9/HD/84Q+xbNkyPPXUU/jkJz+JRx99FNdccw3+4R/+AR/+8IexYcP4HfTi8TiOOeYY/O53v8O6detw11134dJLLwUhBH6/H7/61a8QiUQwNjaG4447DhdccMG0dEu1Wg1XX3017rvvPnR3d+Puu+/Gddddh5/85Cf45je/ia1bt8Ln8zU9EJwIwxCE/Ryy5Rp8AQ3zDqxi6yvjZ4qHtgoo51kcsKoeWHnH73PHeFn+t18UceTpey8kbz4fhOBTseiQ+jOh02uW43rCPg7zGuwUB5ZI475uZLsPi/TPiJuCYqesb+effz62b9+OgYEB3H///QDo2jXZOgAAmUwGjz/+OADgueeeM9e3B+/fiFNPPwtikMPXvnIVvvfD7+GAZQfiuWf+ii9cew3u2fg7fPkL/4zLPvhxnHLilXjw4e9N+9+3dOlSqKqK0dFR3HfffYhGo3j66adRrVZx4okn4uyzz276N+7p5PCBD3wAd999N8477zxIkoRHHnkEt956KzRNw8MPPwy/34+33noL69evxzPPPINvfvOb+M53voONGzcCoAE3ADAMg3Xr1uFXv/oVrrjiCjz11FNYvHgxent7cdlll+Ezn/kMTjrpJOzYsQPnnHMOXnvtNXznO9/Bhg0bcOKJJ6JQKMDvd16DmZCPR+8C6kM8ssOHpYeNX/i3/Mi/w+4tGg49dgFO+OB3cMKBXe28TUtwZaZ4y5YtuOyyyyCKtIuRKIota1c0TRs3MGz8/llnnYV4PI5AIICLLroIT/5lMw5ZcRieeOxRfOfbX8TzLzwBge3E22+9iddfexWXXrgWZ5x0LL73H9/E6Ogg/CLdMa+76GLzSOjSSy81d99G8FooFLB582ZccsklWL16NT7xiU+YO/E///nPpgziQx/60IT/nkYJRaN0QtM0fOlLX8KqVatw5plnYteuXRgZGZnW7+iNN97Ayy+/jLPOOgurV6/Gv/7rv2JwcBAAsGrVKlx++eW4/fbbwXHOX4Aag6ulK0vY9pofN//jx/ZqBvHiE/QIr3fhoPk9L7CaOxo3JPMPrCIYlfH607QrYS45ilv+6YPmGL71nIilq8rgGvYwXoGk+wn7OXQvkMDx6oTOMVKFID3KoXeBpL/HPePulPXtT3/6E1auXIlNmzbh85//PP74xz8iGo1Oug4AdE1r/LOxvv3iF3dj3UUXQ5JzeOmlzfjYRy7HGScdi8/+49UYHaEZxaef/AvefR49abz0svWYSdWNptFn9UMPPYTbbrsNq1evxrHHHotkMom33noLAHDMMceMa2327ne/G48++iiq1SoefPBBvOtd70IgEECtVsPHPvYxrFy5EpdccgleffXVKe9jvDUdADZt2oRPfepTWL16NS644ALkcjnk83mceOKJuPbaa3HzzTcjk8k4cv0M+2knSd6nYnSSTPHIDh+6BiSwnLvmXCPO++1Pg/7+fkQiEVQqFfj9flQqlZa1KytWrMA999zT9L1cLoedO3figAMOwLPPPrvXQ4VlGBxw4DI89PhmPPLw73HrD76I5587G+//0PlYfvAhuH8T3S2P7BAADeAE+oAWRdG0Y7vgggvwxS9+EalUCs8++yxOP/10FItFdHR04IUXXhj3XqeT1b3wwgtx7bXX4rnnnkO5XMaaNWsAAHfccQcSiQSeffZZ8DyPxYsX72X3wnFcU9GC8feapmHFihX4y1/+stfPu//++/HEE0/gN7/5Db7+9a/jlVdeceTkNogGeOzU/7ziuCIevyeGba9246HbNzQ1g3jyQQXA03jqdzdhwUH0+94R/NwR8XMghOoOGRY4+KgSXn9GhKoAD93xA2x9+Rk8dPsGvOuiGzE6KOD4tc2nFt6Gxv2E/TxYFuhbLGH3BM4xu7f6oGkE/UuqYBmC4Bw27pgpTlnfCCE46KCD8Oyzz+KBBx7AF7/4RZx99tl473vfO+E6AADBYND8857r2w//5w6kUkWEQh347e+eRjCyd3GcLLFgORXMDIZsy5YtYFkWPT090DQN3//+93HOOec0veaxxx5rurdG/H6/WaB+9913m0mkm266Cb29vXjxxRehquq0srjHH3883n77bSQSCfz617/G9ddfDwBQVRV/+ctfEAg0n0p+4QtfwPnnn48HHngAxx13HDZt2oSDDz54+v/4NhDycWAYoGe+NKl8YnSHYMos3HQ604grM8UAMDIygquuugpPPvkkrrrqqpa1K2eccQZKpRJuu+02AFSI/0//9E/46Ec/au7YH374YaRSKZTLZfz617/GSSeeiOGh3QiIIi6+dD0+dtVn8Oqrz2PhouVIjo3hmb8+CVkiKBUUDA41NxYwMsWhUAjHHHMMrrnmGqxduxYsyyISiWDJkiX45S+pPlnTNLz44osAgBNPPNHMAN9xxx0T/ntCoRBOPfVUXHnllU0FdtlsFj09PeB5Hn/4wx+wffv2vd67aNEivPrqq6hWq8hms3jkEeoBu3z5ciQSCfNhWKvV8Morr0BVVezcuROnnXYavv3tbyOTyaBQKOx1XSdhZAw/t3YVfvDZJQDGAKzD5o134tqzl+v/nYVC5kAA/2d+/3NrV6HDC6zmDI5lEGxor33oMUUUsxz++d0fxuaNd0LTNGzeeCe+eeVtAFSsPqVZVuEVSLofw1JxYGkVu7f4oGl7v+b/t3fn8VGV9+LHP9/ZMtkTQgJZQBYBgWQSQohQUBERFRGrYgXRWqnXKtdqbfHa/sClWm1/6q9qlZ9e64ZIcWFRf9r2B1QpiFcLsm8CYi67RgQiIQSSPPePmQxZJgtJmHMm+b5fr7xIZs7M+TLL93nOOc/zfXZt9Xdeup9znLgol+VlrU6XHdq34cOHs2/fPmJiYrjhhhuYNm0aq1evbrAdCKVu++Z2u0hOiSczsyf/b+ECwN++bdrgbx8Lzj2Xdxf8BZenivlvvRHyOesqLi7mtttu44477kBEuOSSS3juuec4edJfhnHbtm2UlpY2+TwTJ07klVdeYfny5cEO9ZEjR0hPT8fhcDB79uzgBL34+PgGx/6KCFdddRW//OUv6d+/PykpKQCMGTOGZ589NSm0+qTXl19+SU5ODvfeey8FBQXB+UN2Ul2xqUv3ExwoCp1Dy8uE4r1uMnv7T6JppzjMFixYwMyZM8nNzWXmzJksWLCgVc8nIixcuJC3336bPn360LdvX7xeL48++mhwmxEjRnDjjTeSl5fHNddcQ2HhELZu3sRlo87johHn8p/P/Z4pU6ZzsiyaF1/7Cw/fP50LfzCY66/PY+Om2rNcay7ccd111/H666/Xuuw0Z84cXnrpJXJzcxk4cGBwQtzTTz/NzJkzGTJkCEeOHKExkyZNYt26dbUmvk2ePJlVq1ZRUFDAnDlzQh6RduvWjR/96EfBIRGDBg0CwOPxMG/ePO69915yc3PJy8vjk08+obKykhtuuIGcnBwGDRrE3XffTVJS0mm8+uFXfRl+xqwl5F94GeL4KzAOlyeR/FFX8Kvn3iGz9wOBrecHFwmY8do/OsQqhHZWc0W6fgWlOF1VpGb9L9xR/o6Qy+PF472VntklJKacqvDidgoJEZqo1SnVl2UzepVz9IiLku/qn1LctdVLYueTJKZU1hoqFSns0L4VFBSwYcMGCgsLycvL45FHHmHGjBkNtgMNqdm+OR2CCPzhsdd4641XOH9IHucXDuLvf/WPzb1n2m94++0XmPSjIXxf0nD7VlZWFizJNnr0aMaMGcMDD/jz9S233MKAAQPIz88nOzubn/3sZ/UqYYQyZswYli1bxujRo/F4/Dl+6tSpzJo1i6FDh7Jt27bgmWafz4fL5SI3N5cnn3yy0f9ztT/96U+sWrUKn8/HgAEDeP755wF/udTs7Gxyc3OJjo7msssuazLWcKvu4Hbre5zD37opCTG5ec92L8ZIcJGrSB0+ISbUYfYZVlBQYFatWlXrti1bttC/f/+wx9Ja35WWc7Ly1Gt4cL+bsqMO0nuWc/jbrzlWkonLXU56z9ofopRYT4crDWWn9/ibkuPM+WwXAPOefoBPPigGlgA/5Qfjyrhq6oPMuCaJ8rIvcHkupvLkCYZdPpEJdz7I1At7E+WKnMux7c2iTQfYtK8k+PfsR7uy/mMHlRVZuDzHqThxAfD/mTTtAEPGnNquc3wUNw49q9X7F5HPjTEFrX6iCBIqZ1vlu9ITzPqkyD8P4K7u/HjGPvLOr31l6tGf9CC9Vzk337+f7MxELh7QxaJo/eyU+6x0tLyC0vIKDhe7+P6QE1hDXGISpSWHAmOCE4E+wFbgKCJSb4JgR2T156eisopnP9rBzo1enrm7O1N+u5fsYbXPvi+dl8R7L6Tx2ze/JK0L3HZBb4uiDa25ebtj9crOgLo1FBNSKjCmin07qzhWkgo4qDi5k93bNrJnu/8Sk4R4nAqvmmcbvz98kB+M60Jat8N4vP+bw8XHWDo/mfKyNPoX/hd3Pf1WcJGAeK9LO8QWqzt85cJrD1FZEUdGrzlMfXweMQkv4PZ8w6CRtS9vpugZ/nahelx5tz7HiYquYsfamFr3l5Y4+Hafh+79jge3V/bgFGHP9k18f+gr/C1hLEePfMepk3PxQBUiZcTEJ5KdnWNdsCrI5XQQ43GS2bsch8Ow+4v6Y6t3b/OSlHqS+OTKWgtkRRrNFq3kdDiAU5MF3B5Dp64nOPS1J/BF/xKRE0THJZKUmh54jETcGLf2JsrlJDbKSWl5ZbD4/1ebSpg5rRu7vpjH5s9c+EZ8z033/RARuObn/ktzOlHLenVL4mX1KWfY5Yf5rw8u5+X7KzhW4uKWh/fWWigH0GEv7UR1A11qKumZXcaOdbUnLlWXV6xegTISh0+0V06HkN6zL4e+KabsKEAcIkeJjksAEY6VxALHMKYSh9NJVJR+Z+0iweumtPw46T3L2RWiU7xrm5du1Qei0ZHbtYzcyG3CFeKMb2yCUF62i9Ij3yEiGGNwOJw4A9UYXE7tENtBUrSH0vJTdT97DjzOv/1uL8sWJtGl+wku/fFB6h67pMRpkrZacmz9Ts7V//4NCZ0q2L3Ny7CxRxhwbv2JNXqmuP1IjHZTWl7J2bnHeH9lKiUHnSQExo9/sSoWj7eKHoH61FpX3D6cDsHp8lcPgWNAAsbsx+FwUlFRBcQRm1COSCeqKpseB6zCJyHazf4jx+nW7zjrlsVTVUmwQkjJd04O7vMwbKx/HLieKe7AGhoGUVVZQVxiJ2ITO1F65Dsqa3zBXQ4dtWIHybEe9h6uXQy/3+Bj9Bt8rIFHQEps6BJQKnySYzzBsmzVnC645MbvGn1cSpy+d+1FYrSHfYeP03eQ/7u6+V+xDCjcwaxHfsXBfcvoM+hYsD611qa2D6dDEKCysgKPt5wTx5OITUilsrKcmPgeHC8VYhOFqOgMolzaTtpJ9cFln9wyPv1rEltXHePDt27lx9OfZNvqXgDB72MkH4hqp7iVGuoUd87oHvzd481o1mNUeHUKccaxKXqm2Hpup3+Z7e+PN/9MktMhJEVwola1VXd0M88uJzXzBCsXJ7Bn+//lq40JQBT5I/0HSB6XgxiPNnN24nQInTO6c/KEcKBIcLkzSEip5JvdTlzuKjxeE9xO2Ud1R7fv4FJEDH9/7Sv27vDXhT/2/X8Sl1RBRu/yWttGIj0UayWHSJNrs9cVasiFCr/THR8sop1iuzjd8cGdYj049HsHgIg8LCLrRWStiCwSkYymH2Uv1Y2uCBw8MJ2vNsbwyfuHgAeA/cz+fS/+Y5wvohvn9qq6s+v2GLwxlXx/yMWRg07Ky5zEJVUGh6xpp9heqr9Lv70+G2M+ZM/2nEBd+PdZ+08HpSUvUX0RPJKvzminuAYRqbV0ckVFBampqYwbN67Rx53OGGGtPGEfp9uxive6tfKETZzuIhyd9WCmpseNMT5jTB7wPnC/1QGdrpoHtL9+6SqiYvYAfwNG4HTdT/6oi5nx2j8iunFuay1t39pazfYvKc1/tafkoBuPt4q4pFN1xfXkkb1UV2yaMWsJPfqvAc4GfojDeScQyy0PDwX8JwojtUYx6PCJWmJjY9m4cSNlZWVER0ezePFiMjMzm3ycyyGcaOY+XE6tPGEXidFu3E6pVWe6Mdqxso/Opzm2u7OOJw4yxpTU+DMWCH+x+laqOdmyc0Znsof+ns8/zMTh3EJV5Ry8MRNJ6JRKJ60WE9TS9q2t1ezsuj2Grj3KOXHcQVRMVa2JzXryyF7io1y4HEJCShrpPdZQtGUzMIeqSg/JaavpPyQO8FeeiOT3Ts8U13HZZZfxwQcfADB37txaSySXlpYyZcoUhgwZwqBBg4KrzO3d9d9ceelFXHzeMC4+bxgrP/Mvf7li+TKuunwMP71xEiMKcpl6y09waofYNkSETqfRuUrVjpVtdDrNA5TUeH3vahKRR0RkNzCZCDxTXF1SsdqJ8u0Mv2ITdz97bbCmONSvad3RtaR9Kyoq4rzzziM/P5/8/Pzg6nVLly5l5MiRTJgwgXPOOYfJkyfTnMXAnHUmmjtdEB1XRc2bJcR2yloiErzycrSkmPwLX+Ss/kdJSV9N1x6/D24X6WVLbXmm+Be/gMCy4G0mLw+eeqrp7SZOnMhDDz3EuHHjWL9+PVOmTGH58uUAPPLII4waNYqXX36Zw4cPU1hYyOjRo0lP78qb73yA1+tl55c7uG3KTSz65woANq5fxz8//Zyu6RlcMeZCVn32CRddOLJt/3OqxVLiPHxdcrxZ26YlaMfKLk63vFpafP26mu2ZiCwBuoa4a7ox5l1jzHRguoj8BrgD/2Dcus9xK3ArQPfu3evebbnkmFMlFatrjcOpmuJgz9rUkda+paWlsXjxYrxeL9u3b2fSpElUr264Zs0aNm3aREZGBsOHD2fFihWMGDGi0RiaMyzCqWVLbSk51sO3R0/U+L4dBpKAR4LbRPqQJVt2iq3k8/koKipi7ty5jB07ttZ9ixYt4r333uOJJ54A4Pjx4+zatYv09HSm3TmVjRvW43Q62blje/Axg/ILyMjMAmBgTi67d+8K339GNel0LqundrCOlZ153U7ivc2rQBHvdRHt6VhjwY0xo5u56V+ADwjRKTbGvAC8AP5lntsuuraREudhz6GyBu8XCV3TuiNrSfuWkZHBHXfcwdq1a3E6nWzbti34mMLCQrKy/O1bXl4eRUVFTXaKHQ7/5PSqRs4qa9lSe2rOWWA9U3wGNOeI90waP34806ZNY+nSpRw8eDB4uzGG+fPn069fv1rbP/jgg6SldeHDFf+iqqqKs9KSgvd5ok51ulxOB6ayEmUfac28rB7jcepMdptJjY9qVqe4S4IezNQkIn2MMdVH7uOBrVbG01JN1Qy368TYSGzfunTpwrp166iqqsLrPfV9iqrRvjmdTioqmlcm0eUQTjQyl0Mn2dlTc668RHqFJj0cC2HKlCncf//95OTUXnf9kksu4ZlnngmOm1qzZg0AR44cIT0jHYfDwdtv/IXKBjq+IjrJzm5S46PqrVoXStdE7VjZTXPHCet7V88fRGSjiKwHxgB3WR1QSzTV+OrE2NBa1L6l+9u32bNnN9i+nY6mKjbpqq/21Jxha5G+wJV2ikPIysrirrvqtxP33XcfJ0+exOfzkZ2dzX333QfA1KlTeWPObMZedD47d2wnJjY25POebj1jdeZ53c07A9xVzzbaTnPfE33vajPGXGOMyQ6UZbvCGLPX6phaoqkDWq04ElpL2rdZs2YxdOhQtm3bRmwD7dvpaGp4hA6fsKdOsZ5Gv3OxUc6IH6omzZkt2tYKCgpM9UD9alu2bKF///5hj6WtnKys4rvSxguzJUa78boj+wPTGnZ9j/+2YT9bD3zf6DYTBmfRrVNMmCJSzVFaXsELy3Y2uo1DhKkX9sbtbLtGVkQ+N8YUtNkTRoBQOdsOXl3xFYeOnQx53xW56ZydFh/miEKza+6zSkVlFQcbaC8dIlotpg47fX5mfVLUYF/nrJQYrs7PCnNEzdPcvK2HY23EFVjTvTGeNmyYVdtp6vK60yF6Cd6GYqNcTZ7lT0uIatMOsbKXtEauAujEWPtyNtJeunXohK01dsDSHqr8tKq1EJHHRWRrYMnQhSKS1PSj2icRwe1q+OV0OUSXmbWpzOToRu/vmuDVjpVNZSQ1noSzmnhvVWRr6GA1rhkHTMo6ItJgTtVca2+NTU5vD2f4W/vpWwxkG2N8wDbgN60PKXI1dibY00iHWVkrNS6q0WEtWZ20Y2VXmUmND2nplqxDXtqzzKTQ3830Jg6WlPUaOomknWJ7a6yaT3u4otqqT58xZpExproGy6eAPQeThEljHd8o7RTblojQrZGOb4+U1k8sUWdG90bGebudomeK27nUuKiQeTdLD4ZsL9RJJEGHT9hd10RvyKIBsVHto2xpW/bUpgB/a+hOEblVRFaJyKri4uI23K19uJ2OkMs4O0SPfu2uoY5vjMdJejs4+m2vEmPcJDewglK3TjG49HvXrjkcEvLA6CydFGt7bqfUq2TgcTm0bKnNuZ2OkKu7NnXVLlI02WKIyJJATcu6P1fW2GY6UAHMaeh5jDEvGGMKjDEFqampbRO9DXnd9V9Sr9upX3Sb650aF/Lot2+XeH3vbK53WlzI2/vYpPKAOrN6pdY+oO0U6yHZhss7q9pEpN7iKh25OlMkCXXQeVZKB+kUG2NGB2pa1v15F0BEbgLGAZONFfXd2tCBAweYOHEivXv3ZsCAAYwdO7bWkpbN4XU7+fSTjzn/3HwuGnEu+/ft5SeTJ4bcduTIkdixzFFHFO1x0qNz/S/1gIwEC6JRp6Nfl/qdX4/LQe80HfbSEZydFldrCEX/dP3ONqQt2jiA5cuXM3DgQPLy8ti7dy8TJkwIuV1TbVx0jU6wiA4zjBQ9OtfOrSL1b4tUrVrmWUQuBe4FLjDGHGubkPyeXHz6X9TG3H1x30bvN8Zw1VVXcdNNN/HGG28AsHbtWr7++mv69m38sTW5nA7enfcWt//8F0y64cd43U7mz5/fqthVeORmJbGzuDT4d2ZytC4RHAHSErykJUTxTUl58LZzusbbcolf1faiXE58WYmsKjpElNuBLyvR6pCaFO72DdqujQOYM2cO06ZN4+abbwZg3rx5px80/oPXKJeD8ooq4qJcelUuQqQnekmMdnOkzF8jPDMpmrioVnUnbaO1h2XPAvHAYhFZKyLPt0FMlvjoo49wu93cdtttwdvy8vIYMWIE99xzD9nZ2eTk5PDmm28CsHTpUkaOHMmECRM455xzmDx5MsYYXnzxRd5ZMI8nH3uUO/7tZg7u30N2djYAZWVlTJw4EZ/Px3XXXUdZWVlwX4sWLWLYsGHk5+dz7bXXcvToUQB69OjBAw88QH5+Pjk5OWzduhWAo0ePcvPNN5OTk4PP5wt2vBt6HtW0Hp1jg5eAnA7h/D7td5hPezOsV0rwd7dTKOjRycJoVLgN65WCLyuRK3wZegm+AW3Zxr311ls89NBDTJ48maKiola1cXkD+vLM448wYmihtnERQkTI7XaqAu+g7u2nGm9rq0+cbYzpZozJC/zc1vSj7Gnjxo0MHjy43u0LFixg7dq1rFu3jiVLlnDPPfewf/9+wL82/FNPPcXmzZvZuXMnK1as4JZbbmH8+PH8nyee4O0359aqTfzcc88RExPD+vXrmT59Op9//jkA3377Lb/73e9YsmQJq1evpqCggD/+8Y/Bx3Xu3JnVq1dz++2388QTTwDw8MMPk5iYyIYNG1i/fj2jRo1q8nlU08bmpJN/VjJX5Ga0i/IyHUWv1DgKe3YiLsrFJQO7totZ0Kr5XE4HF/XvoqtONqKt27jHH3+cOXNqTyNqaRvXJS1N27gIk5uVyFkpMfRPj6d3auh5HZGofZzvPoM+/vhjJk2ahNPppEuXLlxwwQWsXLmShIQECgsLycryV6HLy8ujqKiIESNGNPhcy5Yt48477wTA5/Ph8/kA+PTTT9m8eTPDhw8H4MSJEwwbNiz4uKuvvhqAwYMHs2DBAgCWLFkSvAQGkJyczPvvv9/o86imed1OLuirZ4gj0fCzOzP87M5Wh6FURNE2TrWEy+mw7ZLOraGd4oCBAweGHBfV2NzBqKhTZUmcTicVFRUNblst1JgpYwwXX3wxc+fObXQ/NfdhjKn3XE09j1JKqY5J2zilmpUC+ZcAAAdlSURBVKZTPQNGjRpFeXk5f/7zn4O3rVy5kuTkZN58800qKyspLi5m2bJlFBYWtmgf559/fvBy08aNG1m/fj0AQ4cOZcWKFezYsQOAY8eONTkjeMyYMTz77LPBvw8dOtSi51FKKdX+aRunVNO0UxwgIixcuJDFixfTu3dvBg4cyIMPPsj111+Pz+cjNzeXUaNG8dhjj9G1a9cW7eP222/n6NGj+Hw+HnvssWDiSU1N5dVXX2XSpEn4fD6GDh0anGzQkBkzZnDo0CGys7PJzc3lo48+atHzKKWUav+0jVOqaWJFaeGCggJTt3bhli1b6N+/f9hjUeGj77FqD0Tkc2NMgdVxhFOonK2aT3Ofag39/LRec/O2nilWSimllFIdnnaKlVJKKaVUh6edYqWUUkop1eHZqlNsxfhmFR763iqlOjLNgaol9HMTXrbpFHu9Xg4ePKgfgHbIGMPBgwfxenWFOKVUx6Ptm2oJbTvDzzaLd2RlZbFnzx6Ki4utDkWdAV6vN7gyklJKdSTavqmW0rYzvGzTKXa73fTs2dPqMJRSSqk2pe2bUpHBNsMnlFJKKaWUsop2ipVSSimlVIennWKllFJKKdXhWbLMs4gUA//dgod2Br5t43BaSmMJzU6xgL3i0VhCs1Ms0HQ8ZxljUsMVjB1ozm5zdooF7BWPxhKanWIBe8XTnFialbct6RS3lIisas7a1eGgsYRmp1jAXvFoLKHZKRawXzyRzE6vpcbSMDvFo7GEZqdYwF7xtGUsOnxCKaWUUkp1eNopVkoppZRSHV6kdYpfsDqAGjSW0OwUC9grHo0lNDvFAvaLJ5LZ6bXUWBpmp3g0ltDsFAvYK542iyWixhQrpZRSSil1JkTamWKllFJKKaXaXER0ikXkUhH5QkR2iMivLY6lm4h8JCJbRGSTiNxlZTyBmJwiskZE3rc4jiQRmSciWwOvzzALY7k78P5sFJG5IuIN8/5fFpFvRGRjjds6ichiEdke+DfZwlgeD7xP60VkoYgkWRVLjfumiYgRkc5WxiIiPw/km00i8lg4YmmP7JK3NWc3Gofm7FP715zdzFhq3BfWnN1YPG2Vt23fKRYRJzATuAwYAEwSkQEWhlQB/MoY0x8YCvy7xfEA3AVssTgGgKeBvxtjzgFysSgmEckE7gQKjDHZgBOYGOYwXgUurXPbr4F/GGP6AP8I/G1VLIuBbGOMD9gG/MbCWBCRbsDFwK4wxREyFhG5ELgS8BljBgJPhDGedsNmeVtzdsM0Z5/yKpqzmxuLVTk7ZDxtmbdt3ykGCoEdxpidxpgTwBv4//OWMMbsN8asDvz+Pf4kkmlVPCKSBVwOvGhVDIE4EoDzgZcAjDEnjDGHLQzJBUSLiAuIAfaFc+fGmGXAd3VuvhKYFfh9FvBDq2IxxiwyxlQE/vwUyLIqloAngf8AwjbJoYFYbgf+YIwpD2zzTbjiaWdsk7c1ZzcYh+bsGjRnNz+WgLDn7EbiabO8HQmd4kxgd42/92BhQqtJRHoAg4DPLAzjKfwfzCoLYwDoBRQDrwQuC74oIrFWBGKM2Yv/SHEXsB84YoxZZEUsdXQxxuwHf0MNpFkcT7UpwN+s2rmIjAf2GmPWWRVDDX2B80TkMxH5p4gMsTqgCGXLvK05uxbN2U3TnB2CzXI2tGHejoROsYS4zfKSGSISB8wHfmGMKbEohnHAN8aYz63Yfx0uIB94zhgzCCglfJeaagmM+7oS6AlkALEicoMVsdidiEzHf3l5jkX7jwGmA/dbsf8QXEAy/svs9wBviUioHKQaZ7u8rTm7Hs3ZEUhzdkhtlrcjoVO8B+hW4+8swnxZpS4RceNPrnOMMQssDGU4MF5EivBfnhwlIq9bFMseYI8xpvoMzDz8CdcKo4GvjDHFxpiTwALgBxbFUtPXIpIOEPjX0kvzInITMA6YbKyrzdgbf0O4LvA5zgJWi0hXi+LZAywwfv/CfzYvbJNI2hFb5W3N2SFpzm6a5uz67JazoQ3zdiR0ilcCfUSkp4h48A++f8+qYAJHHy8BW4wxf7QqDgBjzG+MMVnGmB74X5cPjTGWHF0bYw4Au0WkX+Cmi4DNVsSC/xLcUBGJCbxfF2GPSS3vATcFfr8JeNeqQETkUuBeYLwx5phVcRhjNhhj0owxPQKf4z1AfuDzZIV3gFEAItIX8ADfWhRLJLNN3tac3WAsmrObpjm7DhvmbGjLvG2Msf0PMBb/bMsvgekWxzIC/2XA9cDawM9YG7xGI4H3LY4hD1gVeG3eAZItjOW3wFZgIzAbiArz/ufiHxt3En/S+CmQgn8G8/bAv50sjGUH/jGf1Z/h562Kpc79RUBnC18XD/B64HOzGhgVzs9Ne/qxS97WnN1oDJqzT+1fc3YzY6lzf9hydiOvTZvlbV3RTimllFJKdXiRMHxCKaWUUkqpM0o7xUoppZRSqsPTTrFSSimllOrwtFOslFJKKaU6PO0UK6WUUkqpDk87xUoppZRSqsPTTrFSSimllOrwtFOslFJKKaU6vP8BTRRBaIJw9xMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 864x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set into eval mode\n",
+    "model.train()\n",
+    "model.eval()\n",
+    "likelihood.eval()\n",
+    "\n",
+    "# Initialize plots\n",
+    "f, (y1_ax, y2_ax) = plt.subplots(1, 2, figsize=(12, 6))\n",
+    "\n",
+    "# Make predictions\n",
+    "with torch.no_grad(), gpytorch.settings.max_cg_iterations(50):\n",
+    "    test_x = torch.linspace(lb, ub, 500)\n",
+    "    predictions = likelihood(model(test_x))\n",
+    "    mean = predictions.mean\n",
+    "    lower, upper = predictions.confidence_region()\n",
+    "    \n",
+    "# Plot training data as black stars\n",
+    "y1_ax.plot(train_x.detach().numpy(), train_y[:, 0].detach().numpy(), 'k*')\n",
+    "# Predictive mean as blue line\n",
+    "y1_ax.plot(test_x.numpy(), mean[:, 0].numpy(), 'b')\n",
+    "# Shade in confidence \n",
+    "y1_ax.fill_between(test_x.numpy(), lower[:, 0].numpy(), upper[:, 0].numpy(), alpha=0.5)\n",
+    "y1_ax.legend(['Observed Values', 'Mean', 'Confidence'])\n",
+    "y1_ax.set_title('Function values')\n",
+    "\n",
+    "# Plot training data as black stars\n",
+    "y2_ax.plot(train_x.detach().numpy(), train_y[:, 1].detach().numpy(), 'k*')\n",
+    "# Predictive mean as blue line\n",
+    "y2_ax.plot(test_x.numpy(), mean[:, 1].numpy(), 'b')\n",
+    "# Shade in confidence \n",
+    "y2_ax.fill_between(test_x.numpy(), lower[:, 1].numpy(), upper[:, 1].numpy(), alpha=0.5)\n",
+    "y2_ax.legend(['Observed Derivatives', 'Mean', 'Confidence'])\n",
+    "y2_ax.set_title('Derivatives')\n",
+    "\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb
new file mode 100644
index 000000000..a956a6726
--- /dev/null
+++ b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb
@@ -0,0 +1,311 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# GPyTorch regression with derivative information in 2d\n",
+    "\n",
+    "## Introduction\n",
+    "In this notebook, we show how to train a GP regression model in GPyTorch of a 2-dimensional function given function values and derivative observations. We consider modeling the Franke function where the values and derivatives are contaminated with independent $\\mathcal{N}(0, 0.5)$ distributed noise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import gpytorch\n",
+    "import math\n",
+    "from matplotlib import cm\n",
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Franke function\n",
+    "The following is a vectorized implementation of the 2-dimensional Franke function (https://www.sfu.ca/~ssurjano/franke2d.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def franke(X, Y):\n",
+    "    term1 = .75*torch.exp(-((9*X - 2).pow(2) + (9*Y - 2).pow(2))/4)\n",
+    "    term2 = .75*torch.exp(-((9*X + 1).pow(2))/49 - (9*Y + 1)/10)\n",
+    "    term3 = .5*torch.exp(-((9*X - 7).pow(2) + (9*Y - 3).pow(2))/4)\n",
+    "    term4 = .2*torch.exp(-(9*X - 4).pow(2) - (9*Y - 7).pow(2))\n",
+    "    \n",
+    "    f = term1 + term2 + term3 - term4\n",
+    "    dfx = -2*(9*X - 2)*9/4 * term1 - 2*(9*X + 1)*9/49 * term2 + \\\n",
+    "          -2*(9*X - 7)*9/4 * term3 + 2*(9*X - 4)*9 * term4\n",
+    "    dfy = -2*(9*Y - 2)*9/4 * term1 - 9/10 * term2 + \\\n",
+    "          -2*(9*Y - 3)*9/4 * term3 + 2*(9*Y - 7)*9 * term4\n",
+    "    \n",
+    "    return f, dfx, dfy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up the training data\n",
+    "We use a grid with 100 points in $[0,1] \\times [0,1]$ with 10 uniformly distributed points per dimension."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xv, yv = torch.meshgrid([torch.linspace(0, 1, 10), torch.linspace(0, 1, 10)])\n",
+    "train_x = torch.cat((\n",
+    "    xv.contiguous().view(xv.numel(), 1), \n",
+    "    yv.contiguous().view(yv.numel(), 1)),\n",
+    "    dim=1\n",
+    ")\n",
+    "\n",
+    "f, dfx, dfy = franke(train_x[:, 0], train_x[:, 1])\n",
+    "train_y = torch.stack([f, dfx, dfy], -1).squeeze(1)\n",
+    "train_y += 0.05 * torch.randn(train_y.size())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up the model\n",
+    "A GP prior on the function values implies a multi-output GP prior on the function values and the partial derivatives, see 9.4 in http://www.gaussianprocess.org/gpml/chapters/RW9.pdf for more details. This allows using a `MultitaskMultivariateNormal` and `MultitaskGaussianLikelihood` to train a GP model from both function values and gradients. The resulting RBF kernel that models the covariance between the values and partial derivatives has been implemented in `RBFKernelGrad` and the extension of a constant mean is implemented in `ConstantMeanGrad`.\n",
+    "\n",
+    "The `RBFKernelGrad` is generally worse conditioned than the `RBFKernel`, so we place a lower bound on the noise parameter to keep the smallest eigenvalues of the kernel matrix away from zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "class GPModelWithDerivatives(gpytorch.models.ExactGP):\n",
+    "    def __init__(self, train_x, train_y, likelihood):\n",
+    "        super(GPModelWithDerivatives, self).__init__(train_x, train_y, likelihood)\n",
+    "        self.mean_module = gpytorch.means.ConstantMeanGrad()\n",
+    "        self.base_kernel = gpytorch.kernels.RBFKernelGrad()\n",
+    "        self.covar_module = gpytorch.kernels.ScaleKernel(self.base_kernel)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        mean_x = self.mean_module(x)\n",
+    "        covar_x = self.covar_module(x)\n",
+    "        return gpytorch.distributions.MultitaskMultivariateNormal(mean_x, covar_x)\n",
+    "\n",
+    "likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihood(num_tasks=3)  # Value + x-derivative + y-derivative\n",
+    "likelihood.initialize(noise=0.01*train_y.std())  # Require 1% noise (approximately)\n",
+    "likelihood.raw_noise.requires_grad = False\n",
+    "model = GPModelWithDerivatives(train_x, train_y, likelihood)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model\n",
+    "The model training is similar to training a standard GP regression model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iter 1/50 - Loss: 110.384   log_lengthscale: -0.367   log_noise: -4.714\n",
+      "Iter 2/50 - Loss: 107.250   log_lengthscale: -0.439   log_noise: -4.714\n",
+      "Iter 3/50 - Loss: 103.263   log_lengthscale: -0.514   log_noise: -4.714\n",
+      "Iter 4/50 - Loss: 99.708   log_lengthscale: -0.590   log_noise: -4.714\n",
+      "Iter 5/50 - Loss: 95.151   log_lengthscale: -0.667   log_noise: -4.714\n",
+      "Iter 6/50 - Loss: 90.692   log_lengthscale: -0.747   log_noise: -4.714\n",
+      "Iter 7/50 - Loss: 87.713   log_lengthscale: -0.828   log_noise: -4.714\n",
+      "Iter 8/50 - Loss: 83.105   log_lengthscale: -0.911   log_noise: -4.714\n",
+      "Iter 9/50 - Loss: 79.508   log_lengthscale: -0.994   log_noise: -4.714\n",
+      "Iter 10/50 - Loss: 74.017   log_lengthscale: -1.078   log_noise: -4.714\n",
+      "Iter 11/50 - Loss: 70.958   log_lengthscale: -1.157   log_noise: -4.714\n",
+      "Iter 12/50 - Loss: 71.528   log_lengthscale: -1.224   log_noise: -4.714\n",
+      "Iter 13/50 - Loss: 66.021   log_lengthscale: -1.260   log_noise: -4.714\n",
+      "Iter 14/50 - Loss: 63.496   log_lengthscale: -1.273   log_noise: -4.714\n",
+      "Iter 15/50 - Loss: 59.752   log_lengthscale: -1.266   log_noise: -4.714\n",
+      "Iter 16/50 - Loss: 52.506   log_lengthscale: -1.247   log_noise: -4.714\n",
+      "Iter 17/50 - Loss: 52.274   log_lengthscale: -1.218   log_noise: -4.714\n",
+      "Iter 18/50 - Loss: 45.548   log_lengthscale: -1.187   log_noise: -4.714\n",
+      "Iter 19/50 - Loss: 43.033   log_lengthscale: -1.160   log_noise: -4.714\n",
+      "Iter 20/50 - Loss: 37.667   log_lengthscale: -1.144   log_noise: -4.714\n",
+      "Iter 21/50 - Loss: 34.453   log_lengthscale: -1.146   log_noise: -4.714\n",
+      "Iter 22/50 - Loss: 30.147   log_lengthscale: -1.163   log_noise: -4.714\n",
+      "Iter 23/50 - Loss: 31.184   log_lengthscale: -1.193   log_noise: -4.714\n",
+      "Iter 24/50 - Loss: 22.408   log_lengthscale: -1.232   log_noise: -4.714\n",
+      "Iter 25/50 - Loss: 26.248   log_lengthscale: -1.272   log_noise: -4.714\n",
+      "Iter 26/50 - Loss: 17.774   log_lengthscale: -1.308   log_noise: -4.714\n",
+      "Iter 27/50 - Loss: 14.621   log_lengthscale: -1.333   log_noise: -4.714\n",
+      "Iter 28/50 - Loss: 11.387   log_lengthscale: -1.346   log_noise: -4.714\n",
+      "Iter 29/50 - Loss: 11.609   log_lengthscale: -1.356   log_noise: -4.714\n",
+      "Iter 30/50 - Loss: 7.574   log_lengthscale: -1.354   log_noise: -4.714\n",
+      "Iter 31/50 - Loss: 4.772   log_lengthscale: -1.344   log_noise: -4.714\n",
+      "Iter 32/50 - Loss: 1.492   log_lengthscale: -1.326   log_noise: -4.714\n",
+      "Iter 33/50 - Loss: -3.760   log_lengthscale: -1.301   log_noise: -4.714\n",
+      "Iter 34/50 - Loss: -3.007   log_lengthscale: -1.292   log_noise: -4.714\n",
+      "Iter 35/50 - Loss: -8.198   log_lengthscale: -1.299   log_noise: -4.714\n",
+      "Iter 36/50 - Loss: -15.006   log_lengthscale: -1.317   log_noise: -4.714\n",
+      "Iter 37/50 - Loss: -11.899   log_lengthscale: -1.353   log_noise: -4.714\n",
+      "Iter 38/50 - Loss: -19.430   log_lengthscale: -1.397   log_noise: -4.714\n",
+      "Iter 39/50 - Loss: -18.595   log_lengthscale: -1.443   log_noise: -4.714\n",
+      "Iter 40/50 - Loss: -19.237   log_lengthscale: -1.472   log_noise: -4.714\n",
+      "Iter 41/50 - Loss: -23.092   log_lengthscale: -1.495   log_noise: -4.714\n",
+      "Iter 42/50 - Loss: -22.628   log_lengthscale: -1.499   log_noise: -4.714\n",
+      "Iter 43/50 - Loss: -23.558   log_lengthscale: -1.497   log_noise: -4.714\n",
+      "Iter 44/50 - Loss: -26.297   log_lengthscale: -1.487   log_noise: -4.714\n",
+      "Iter 45/50 - Loss: -32.775   log_lengthscale: -1.480   log_noise: -4.714\n",
+      "Iter 46/50 - Loss: -28.115   log_lengthscale: -1.480   log_noise: -4.714\n",
+      "Iter 47/50 - Loss: -28.947   log_lengthscale: -1.488   log_noise: -4.714\n",
+      "Iter 48/50 - Loss: -36.858   log_lengthscale: -1.501   log_noise: -4.714\n",
+      "Iter 49/50 - Loss: -36.496   log_lengthscale: -1.522   log_noise: -4.714\n",
+      "Iter 50/50 - Loss: -37.434   log_lengthscale: -1.548   log_noise: -4.714\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Find optimal model hyperparameters\n",
+    "model.train()\n",
+    "likelihood.train()\n",
+    "\n",
+    "# Use the adam optimizer\n",
+    "optimizer = torch.optim.Adam([\n",
+    "    {'params': model.parameters()},  # Includes GaussianLikelihood parameters\n",
+    "], lr=0.1)\n",
+    "\n",
+    "# \"Loss\" for GPs - the marginal log likelihood\n",
+    "mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)\n",
+    "\n",
+    "n_iter = 50\n",
+    "for i in range(n_iter):\n",
+    "    optimizer.zero_grad()\n",
+    "    output = model(train_x)\n",
+    "    loss = -mll(output, train_y)\n",
+    "    loss.backward()\n",
+    "    print('Iter %d/%d - Loss: %.3f   log_lengthscale: %.3f   log_noise: %.3f' % (\n",
+    "        i + 1, n_iter, loss.item(),\n",
+    "        model.covar_module.base_kernel.log_lengthscale.item(),\n",
+    "        model.likelihood.log_noise.item()\n",
+    "    ))\n",
+    "    optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Making predictions with the model\n",
+    "Model predictions are also similar to GP regression with only function values, but we need more CG iterations to get accurate estimates of the predictive variance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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tl/dlZP083S+i7Ymk9mX49aXTpI5f507ywe69kKdPHD9SXATbmi5f/8jz67NUq8Uyr8cBgAm3dtenTgTyVIkA0GoVtTytTI+TaxHC9FOPUkV/U1XrUleaaMGJXMuIjodo/hZdPRaGzrptltOW7ee7yYI00/Z48tEMnHOx8QA7X18WSd8OhFL4Wx+fXebnFakT0eh46tTaVGU79KGXJukMsIY89byXzVTU6ND3AzcupzdyffKBa4sR5QwRn7DPZ4//3Adyjc7Wg6STJ0lj97ltptFhmmkPSYWfaXRy14wsDq1v7M2qLJB3j/ZsuUbHv9ew9yO+X1HLs1lRVz1orct21NbUhb7REUIIIYQQQjQOTXSEEEIIIYQQjUMTHSGEEEIIIUTj0ERHCCGEEEII0TiGlozAtgLJB5h6N6Lw9XVY20wF7AW2EWEhkBuERpIRHCR1iLhx0vXh0LVfz+qsTfukArmpZ6RsD1EELjmRohctAjHTrMr0U3hfVxKDaMKAKudSte1tbBg6chCN5rq7dpHLzZJvsP0iSTp8nWg7G/5cqj7fA6aKqScT+FbdL99naB+dQyESz7dbcoTHxCqAL7syn4yAvWf4dwb2mLD3A/Y+4JifLar6T+3PbYIrG4aed5/93ggUyBMPsDK2n09GEDFdB/LkAyypQSA2rbTzzE8Lzrl4fDq/blPe0J5c202SFsZfX5YgwsccNr5YrNpuCQP62R9/TQypclv6RkcIIYQQQgjRODTREUIIIYQQQjQOTXSEEEIIIYQQjWN4C403kWtSvI6GrYGNrJP1dVhZxFSUaXTYmtCIRsd7e0WOT443Se7YoesfKmxfbOXGVkxb48umMocuYLdbg0rNQck6zUrmcVVNPftpvNnP/fppBrrTNDqGfJ13ZFz6stxLj+632x1rNxm73rouYg4K5EF5jNTxI5yZg+bWecCeyPn6MrZ+npX5/UisWnd1mF5glZR5/UdEaxMxB+VtR4xHm6NHiayz32maJKwA+B+urIpGh+H1ukD+2U8utx0qbs/tz9052eezh42vlQX3fsC0Nsww9ITbZvsxE1EPMwytotEJ6n+WZornOzOd6528hnm30+wAPH75e8DNQJsTP+qiNl03Qd/oCCGEEEIIIRqHJjpCCCGEEEKIxqGJjhBCCCGEEKJxaKIjhBBCCCGEaBzDUxhuIRfgebUuE+x7AeDXSJ1TpOwht82MRv3xoskI/FVkomfvWcXOjR3Pt02EdXuni9aA89fkKskF7CNlxeQD3iALyM2umGlWRADZbvfR6KpO4X8/Ex0Msu0dRmoB6059P+ZFv0wE7Mdl0Mh3ypUd/yrZzw0LljCAJSiIGIb6RAOHSJ2rid51yp8f29EnTsn99vi1DFzv5ZliagUmjF4jGRJ82SqpUyVhQaesXVon0s52JJZooJ5z2W5mho+JNQB+TN/vtlkygkBSATp2vFk5e4dw7zV7npgL6Nnns4eNLyy4lCdRw1CfjIAlLPDnxmDJTXzygci7F2uHJTqYK1ZcmsuTNe1xCQqmiOn6FDEM9QmceCKPtdI6LKFTxGg0QtWxOqpjXN/oCCGEEEIIIRqHJjpCCCGEEEKIxqGJjhBCCCGEEKJxaKIjhBBCCCGEaBzDS0aQkIvUvPiMKXV9EgGfZOBSZU7Il7zLMYDTru2oUNiLjPcSbeeBSKIDhtcNBgTVcwdzK+I9E7lw0ScfYM6/4040FxWj1SZaG7aov2odxqj2ewTYaLVwdraoOj18xA06NuZ8DIo+ti5y7iXi4W9zMeYRIl5eJn0KJSNwcWEviwsssYIvO0bq+LIjwbZdEgOfHAIAFltF0e8ichGwdyTvlBWjLHMk9yJrVoclMfAC34g4n7mdDxomVvYMMtHAqCRoCLEG4MuuzCUnYO8QbDx7plhyDw8bz9cWN/ecz1X+U7P5Z7iHJQCBf2U4Q3ZkiQZ82Qo7Psv85PcjweI+UubxyQfmSB12LvPFzYtLecxZni6WrSJ/h4okPIkmRdlJVE2iUBV9oyOEEEIIIYRoHJroCCGEEEIIIRqHJjpCCCGEEEKIxjFcjY5f9uvXt3rNDpAbbTLjTbJ21q+nvY8sG/W7MY1Obg8FOKst7CfLmR9xjV+bV8n1OEC+Vpfpj9x6+enzW1mVPQfz9aVek8PMxvxaSra2MrJWnLLhHr/taLy509oeUTbQxkN+ILjNQ+08WJgfc8yoj62X90abbFy6GLOXxKq9xIw0uy8sSkf67Y0/gVxbw7Q2XpPDNDqkbMWVnZ3OxQjepJhpdJaJteqy0+0wHY/X5ET0OKwsor/hJoD9g8XdunRCg9T6jAxryHS9F51h6ANk7Pp3Bv9uAACHyH6H/OPExqWLJ2OkndZs+b2khqH+XYvpWpiJ6IpXFHoHUSCk0SFxADhe3DxJApo3A53Pq+AwKXPnu0U0OquHiteJjXmmd6qi+auTyu9jjjrNQQetyfHoGx0hhBBCCCFE49BERwghhBBCCNE4NNERQgghhBBCNA5NdIQQQgghhBCNY7jJCMrE0cx8y5exjAFE9OvNQEm+gsz7Kpfv82QE/iJGjEanSAcOMdGzNxlkuj5/vkSkOE4upk8+wMRnXthWVei2sREQ5DVJ1J97uY1Gv0eUNYzjflxTKPMC9sX9uWPngf1F1e3+I+TGsWDhy4gZaGRc0hgXuS9eT+xFuQBPUODzA7CEBa5OInXO7M8P6BMN+G0gTz7ADUPLkxEwEbAXWTMRMBPwRxILDFuMz45fRSxcZ1KDukTP25J1ILlkBPe58fsA2S2SjIC9Q+xx8WSKxRNfRl40xo/kCYU89Fn2yQhYIqjchxz5WxO7KixTi4e5qLqrt0EMRM+4OqyPrCw7X8uqrK0WY8zaBEs8kMeOKrFi2PEFqC/5wLATDzD0jY4QQgghhBCicWiiI4QQQgghhGgcmugIIYQQQgghGsfwNDoRItoHVoese/fLWZmOxmtymBwmotFhXfKrztkS3EPM/NSv64/olkgdbga66bbL11ZG13NnplneHBQANty62O2g0Yk8X74sosep2idpdEIwjc4C9hW2H8pcPoE9btQzY925g/kib7/f1ObFrM7upaIyjxn80WXRkfvihxPxAFwnZcszxTXtF1u5UZ7XzTDNDDf6LDf19G35fYD69DdV18/XuV4+Ei9HQevST8PS7cjGJnDOfR57WV7AI5hqdBgHXWy4lr2g+PhBPnciht70vvm2mEaHlWVnHLkqUbwmh1yUJScgZH1kn8++Hom5qyvFOLQxUW42zGDX25flEa8+RtX40z+3hlS5LX2jI4QQQgghhGgcoYmOmd1kZl80sxNm9iry9583s8+b2WfM7E/N7PH1d1UI0QQUT4QQdaBYIoQoo3SiY2YtAK8H8BwANwB4kZnd4Kr9NYAbU0rfBuC9AH6z7o4KIUYfxRMhRB0olgghIkS+0Xk6gBMppXtTSmsA3gnglt4KKaUPp5QeXaR+N4Cj9XZTCNEQFE+EEHWgWCKEKCWSjOBqAPf3bJ8E8IzL1H8ZgA+yP5jZrQBuBYBjhwNHZ3+PKP8J3rCT7eYTDbDEA74dVpZLd2PHT6TQvI6sj1qwiHEeIyK222SGof0U3nsBIhMkRpII1GX8WXW/5iUj6Es82XNsFl/CdYW/j7sEHFPIEwZ4I12WtIPvV6w33sozgLRni4N3fJYlBCm/CUysHhGCR8T4TPjv91ujdfLjrwYSBviyqIFnltykogi4KlVjY6Sf/qmIioerPjuxtov7Rc5/CEaBtcUSoBhPjgB4xF06L4VnEnufyoRdtTyNBzEnZ6L6QNKhyoktIp97lMhbE8us4GFXKpBCasUlI6j6OV/jo1tX3GHt1GfqWd5OP8fzoBOwRKJ3bhkLnv7AzF4C4EYA38P+nlK6DcBtAHDjk6x6CgUhxKjSl3hy+MarFU+E2FnUFkuAYjx5iun9RIimEJnonAQKeVuPAjjlK5nZswH8CoDvSSmxJMhCCKF4IoSoA8USIUQpEY3OPQCuN7NrzWwcwAsB3NFbwcyeCuCNAG5OKfl080II8SiKJ0KIOlAsEUKUUjrRSSltAHglgA8B+AKAd6eUPmdmrzWzm7vVfgvADID3mNmnzOyOSzQnhNjBKJ4IIepAsUQIESGksEwp3QngTlf26p7fn33FR94FYNqV+W3i7J2VTZI65Kx8ggB24pE6EZgbsi9jbRsr9Hq0SB2iheNu48WyiMDYO5RfsmyzWLbFkhFUTRgQ2a+qqN+3VbXOoBMGjE4ygr7EkxVM4AS+6bJ16hJmb4f9qgrRY2337/hVjlXnflWpkgwBqE+YHLmX7JpsZnXy+zbIhAV105d3E3SEPl5WH0koxJIVeUJJjtgtCcRr9gz0d6z4TkWuQKSdARM4fJ3Pt28rGk+qwJ+J/iUy2Y6EDEOFEEIIIYQQYpTQREcIIYQQQgjRODTREUIIIYQQQjSOwS+qfRSm0Zkp2WZle0kdUnbgbHH7HEky6e2o2GpTtr7W62+YIZjvEut2dj1YWaQO0S0xHY3X30R0PKwONR30mpwVIriqqnWpou3ZDvqfKhqZqm1H2hrysug6WcVEZhiam2MyfVnx2WWaCq83A4C1Fbcf0aBtbpSHV2ak22o7XUWb6CpcnfHJ3IyUmZh6Q1RvmAoAUy7KRQxTWT3Wdn78vJ0Jsl9Ej+LXnVddYx4xMeV6xnLz1cha/Oia+si99HVa9PjlGZe5/qdVWmdUMXCtbS/91PD2Ezou/Mkw7TPFK5uZXXrk7CL7kXZ8vyMm89H9HFUNNCPaHm6uzIyTy3VxkbhXl36zTm1RP3VK+kZHCCGEEEII0Tg00RFCCCGEEEI0Dk10hBBCCCGEEI1DEx0hhBBCCCFE4xheMoIWkGaLRbbf1fHbrOwAqXMuL9p7obh99P7Ldw/gCQNYMgIP6/Yht301q3SQlPnzY/u567hC6iySFAm+jNW5iKnCdtQw9OJScT+sWN6pfiYM8GVLFduu0zC0X3VYvTrbHgHWtiZw34XjhTL/DG4tkEwe/rlYII2zMr9f5PkKPidbbnudRWkvFmaJW+YCZazOfDENy+TcYt7MbH5R9mDxstudsuKFYokOIskPmPDeC2yriuNjxsl54gEfK1k9lhDDE0k8AOTXwF9bIL+W7NpyrjxBQT/FxIPGkMvj/fsAec3I6rChy5IVZVJ89phUfFuLJPLI4glLRsDKVvzZsBeUyAdN5GWPvJFFElhFzoXU8QlfokQSkLD44YkkJWGt+P1YcheWBCZ/TupLvhAxXO5nMhN9oyOEEEIIIYRoHJroCCGEEEIIIRqHJjpCCCGEEEKIxjE0jc5G2/Dw/uJC1P0X3KL282THCyXbAF9e7JYbes0MAOw+Xdw+S9qJrFpk2p5DXn9zhFQ6Fihj+7mTWZiezaoskMX4i25B6xLV6BRXD0d0PACw7gwVQxqGqjoaVqeKhiLadkR7UZWqbe0wTY5na7WNpRNXFQvP4PLbrOzBQB1WFtHxRMxnGSxK+7XoVGsTKDtM6hwuGvOtHM3Xzz94OC9bOFxULTAdzz53oebIhWPrt3c7bQmr49edR9eY+zXlEf0N1zzmAoFltx+LlRHjTaat8RqoZXIt/fWNXFtONVPRUaW9C9jvhDP73bsG0+t6rQ2zymSy4v3+tkSMwcvlXgDy+8L0baF4wsoePOoKmHIpYhjK3pr8SxOp4/sUMZkHck0OibHeuDlqQOzHWESPw0yKI7A++VgRjSdV4ifT47Dz9edXVcdTFX2jI4QQQgghhGgcmugIIYQQQgghGocmOkIIIYQQQojGoYmOEEIIIYQQonEMLxkBxnDaqejbR04VtveuFI3rACDTVUUF1gGx316nEtzLkiEwTWZESOgViCypwLWk7Bq3TRIWrLg6DxHn0bNEAnnWKZMjpqI+OQEALF/Iy7Dk1H51JRVg9VidSDKCSFlEQB41DK0y2qqO0J2WnGANwElX5hML+L+zMpaMgO3n67GEBdn1ZaJvJmn24l32EDhROzPFY4kGvHbYbwPAcbcdHDsrG8UEBWc3SL9dGOqngL0VFLN68SxLGOCTubDkLmdI9oeFs8V66wvELnLDmSm3U1ZljJi2HjhQfOhYvyMiZ3YP6jJCLhI5AAAgAElEQVRfHVV2tYEp9zF69Mvl+0UMQ1kipAP+45llLPDvFWTMcyPGYiCaYjHHP84skQmLJw/6M76eVHqIlHmYjerx4mYk4QrrN9svMxrN3zXHJ3JTTQ83Fx4vrZML//N3KGbE7ttixp8elmyEmTnPuLJI8gVmgLxMzsXHpvLUJjmGPC5G0Tc6QgghhBBCiMahiY4QQgghhBCicWiiI4QQQgghhGgcmugIIYQQQgghGscQkxG0ccYp7lotJ356Qi5i2wuSoMDDzsoL+WZJHW/qy0S4EU0mS0bgjcSZIpElI/BlpM6p6aJK0Cd5AICHSJlPUODvB5CLblnCgiUmsK2SDCCSsCDatjcJjzjXR9tmfYrgn0v2nFapE6VJyQc86yhPPnAf2S9Sh5VteIHnA6SSDyjMNTwQz4i4MwsoKySg3EcyDUTGnCf6DDqB7/pkHheWZ4rX7eJELoxmzu3jTvTKRLibLjhHnbW9UJbFOB8HT63m2WTO30fU2v75YnHI34O2ZVXW53NX+AePF/u5djwXBvvP1Aly3di19MkHWDIC738edY4fCcYAn9PnkHssd5/Nd3vE1fFpRQBgP3v38I+Tf18A8neW/HZT/H2ZYklRvIg/ksgEyJOwnGQvNuxkPORK+YQB7Pi+jPU7kKBgbCaPQz6JA4PFmGWSFKRsPza+WBIBnziF3ksHSz5xAPnDe8DdTNYn3292rix+5vEk348luupFyQiEEEIIIYQQogdNdIQQQgghhBCNQxMdIYQQQgghROMYmkZnHWP4mluYuum6s9nK1z9uXF/U7eyfJIvMIxqZ3FMzX0J/gdSJGEGytbN+fS0zDA2YiH7l4FVZlfudi+j9mcsoNxH1BncL2JfV8WUL54n71gJxLvNr0ftp6snWvfsyZugY2Y8dvy6YyaMvY3Xq1O00hU2U67LYM+B1PcwwNNPjALlwh2l0vMaQaXQiwqmARocajxLOuEXtfh08kK9pr6pvW8m1JpsbxZi+OZHHeP85wGBr4705JtOMMIM7b/DH1or7defnz5A4yIxl73Pb7PnyH2FszNM4VLy+5yZzjeXc1cWbx9bPM/PAqL6pl4g56cgwDmrO3cteZjru3xnYJYlodJjUxcu0yPHZPfD6CKb9GJt/pLC9fjTXhGVGwkD+XLKhu+T0NyzkReIQ0+h4TU5Fjc7EZK4LZHo2D4snfuz4+ALEdDxco1M83hwNzkXY+D6CU1nZwfPFz6cx4uq57k53cTYPVuP0gpeTXzePNDpCCCGEEEII8Q000RFCCCGEEEI0Dk10hBBCCCGEEI1DEx0hhBBCCCFE4xhqMgJvYumFdN4cCcjN3ZavyU1FD+zPRb+TXotPzL7wiNuu0zDUCxCJh9YKSUbgzUB94oFOWTH5wCmS1YCZiHrDUL8NAAubRdXeSsQclJXVmYwgkjDAC88jQnQAueCNCci98JuZPjKxoRM5rxDBZ0SYHC3bSWwhv3Zl20DsGSRizryMPSe+jLUTMQxl+GeQJSMItM2EwZH8CFXNbgNEjPr6CUuGkImOV0jGmcjzFTEMjY7vLPlD3if/GcqSDETKqiQnGGnGkCcI8I8FSyrgkxFEzMtBjsWSJbmPZy8MB/iz65MRMAPJuQPFB/PrLBnBdaRPVRJpsOHN9ouYmB4P1CHa+F1zxRu1e5oZhpa/7LHEJT7RwOJq/s6UJTPZyMcXMzHdPFC8v4eyhDc5NBnBufzzyu51BSQZ15i7T/sP5kGvfSzvk08sxpJm+KQNPubkqW3i6BsdIYQQQgghROPQREcIIYQQQgjRODTREUIIIYQQQjSOoWp0vJbEr3dcIuZm3vCMGaCdmX44K5u/tijK2XMsX7e453xxTbuxNdcRjQ5ZO3thtjinXJjIDefOEI2M19Yw/Y0vY3WYYagvW/AuWgAW/FrShbGsTshQkGkf/PUdtGEoMeTKjR8jGh0GM3n0z2p+vzPx1gq53hF2uoFolNB1ighS2P32ZRG3YUbkWSJr6lmZPxzxvczM+5iZHyvLzG5zjdD4ZNGEb4qMQWbUN+Hs48aJnZw3CGVr7H07QK5jYXV8P3fN5P3emiPiC68PYI+Aj4PskWAefK6M9WncXUu/DfDrFNEnNJpxIPPd9p/rbMgxk3EP0+h4CS3T6DhN0PJM7LPBP89MszHvPiC/fvRxeUNnyPH888ziif8sjmp0fFtsDHgTUarRyePQnrniNWBxKKIV9AaeALBwodjxpQdJx086xQl511wn9/fUk4v35brZE1kdr21h99u+mh8Pf+u22bPsn13S770T+fW+eKT4EHitPZBrm/y1NRmGCiGEEEIIIcTfEZromNlNZvZFMzthZq+6TL0fMrNkZjfW10UhRJNQPBFC1IFiiRCijNKJjpm1ALwewHMA3ADgRWZ2A6m3B8DPAPhE3Z0UQjQDxRMhRB0olgghIkS+0Xk6gBMppXtTSmsA3gngFlLvVwH8JribgBBCAIonQoh6UCwRQpQSUcJeDeD+nu2TAJ7RW8HMngrgmpTSB8zsn16qITO7FcCtADB9bB9OO8XdshPdskQDvowJ6OeIOt2L+ve0SDKC/cUyJtyMEDE6ZefGzsUnDDhLFHk++YC/rp2y3DB0AfuK2xfy428tOPVZJPEAK4vUiRjuRdvOBJBMyMbMtnwyAlanajIC7xIbEaczZ9mAddb2TUbQl3iCfceIGN5tR0T1VExL7kH2DLB76cWk3pEY4Kae/tlhqmc/nq8mdUiyCy/e9dvROkwY7ITAM/N5sJhrFct2ExEwK/OxmCUsiMRrZnzJxLql5OEUD2Wu1MDKvHt2qhqGkudy8nAxUcqR2a9ldbyh4BzyRD38ehcF7Ns0OUFtsaRb9xvx5Ng8kHlz+/tynjSS57HIIcmKsqHKQo5LRrDaYg3l+HHBnvcDzkF9/9HTWZ1zSyzGOFiMjRiGss8r31YgIQerMzmXn+/uieIzz5Kb+BjjDS0vVXZxyQntHySf1yfdNosLZMz7eLI4SwzcHTS+5bcX8AkKWB4m/1HEfISJke6eA8U+TEzk19s/p3XGnMg3Ouyt6htvjWa2C8DvAPiFsoZSSrellG5MKd24+yo2IoQQDacv8QTTV9XYRSHECFBbLAGK8eQq9r8FIcRIEpnonEQx0eJRAKd6tvcAeDKAu8zsPgDPBHCHRH9CCILiiRCiDhRLhBClRCY69wC43syuNbNxAC8EcMejf0wpnU8pzaeUjqeUjgO4G8DNKaVP9qXHQohRRvFECFEHiiVCiFJKJzoppQ0ArwTwIQBfAPDulNLnzOy1ZnZzvzsohGgOiidCiDpQLBFCRAhJllNKdwK405W9+hJ1nxVpcx1jeMgpOr1An4mollydh4lii+3ny6aIoNy743IX6WpuuV605s8D4OfiEwacJQLjSMKCM2S/s6vFsqUzRP3mRXKR5ACsjCUa8GWROqws1CemrGOKvLNuu2oygsgi74jjPXO/Jm1H3NW3SYKCfsQT7EIuXo0kGvBDhT1vG0QKsOQV+ux++2cp8twAsWQEbjyzc2NJBK5z28cDdfz2JfabOfr1wvbB6Xx8eTE8SxzD4reP1xHxcBSfoGCGHN/38yCJHd80m4/n5dmiMJmJlzfdwGSfMezc/HViSQX2uEDIHOBZWUQY7PvZHkLCgr7EEqATdo+4Mv+xTkTXobxuLA77tkgeEe9KzxJrsGfH31+ejOBMYftI61RWZ+14/uwutZ02ksWhyLsAuyY++QNr25WNzecJX/bM5i8IPp6w8RURw/uxCwBbPhkBSzRwBuV12Kumq8fiiYcmafEfTUD+OsTq+D6x16oLpA8rW8XtifJ3a79tYAmlYoQMQ4UQQgghhBBilNBERwghhBBCCNE4NNERQgghhBBCNI6hrdpfRzsztvRrCdna4Xxdcr7una/xLrbF1jP7dZtsvWtkHfIGWTvrdTveQBQAFonbltctMf2NNxplOh6v9QGAxQWnB1kgepAqxp9ANf1NVY1OyO+a6SNYmX92ovt5mLbG7xdpmxlKsrWqTkdSLiVrFm3ka7j9cxG5JpG14kC+pnqB6GiWXFnUKC+yNt2HgcOkznFS5nU7rI4vO54PsKuuzjUq827huTchBHKtC4vxdcVmrispL6u37f7pWPznDNMLMB1HlTo7jjEg8932Gh1mBRiJOWzMO/0N0/+kgEaHPV9+PDFd3Lwbq8zQfHU614OcPl483vkZpvN1AS30eQ1iAJ1/7o05M9A9xBw0oktjmj9P5bHLngFfFqlDytiY91BdOTsVX43ViTzfpKzt2opo3f31lkZHCCGEEEIIIXrQREcIIYQQQgjRODTREUIIIYQQQjQOTXSEEEIIIYQQjWNoyQg2iGGoF4QxU08vkuPC1VwQFzED9XUi4lIgF01FkhEwo6dlmqCgeL4+8QCrw4xHF8/nysnM2Kou409WVrVOVSHfyFJVpciSHwR2awpt5AJ9VsfjBa9MYMzajSTpqJr8oIrRKTMHjSQoOFqeaMAnGQBiiQYixs1R48/cwLK+hAETrg+s395EdB8RdEfMT5kZaUTkzJPXlH82+DJWh7W95j6fVokJdqOTGLSA5HL6mB+rLEmJH05R/bq/vD45AYCNwOXm7zXF9yj2nPrxzJ4JJnxvTxRPcPfV+fvY0lzxOb3o3zuCjE/msWJqpnhuUy1mfpvv5+NAZAxG3wfh+zlDHhQf49lnBfssck352NXpU7GfNGFBPpzzZ47lZ/B9YmOAHM4/u5EkCnWib3SEEEIIIYQQjUMTHSGEEEIIIUTj0ERHCCGEEEII0TiGp9HZauPsheIi2PHJ4vrSxRZZk+nWm7I1iky349d9R+qwtZ1sLWe+JjJfTOt1O16zA8R0O8zIy5ctr+ZrYFfYutglZzIZ0cjUqaOpyTQrBtOw7CZl/voyU8+AHoa27ctYO5EhyUxE/X5G6jQYptGJ6G+8ZIHpWqoa4noiehxWxjRCAcPQycPnsrJDs0X9zQGiv/HmgWxNf0SPEomx4XXvASIaHRbTYxqG4nU6glNZnSMXHszKJu93Bbm0KV8Lz7QYB/J7mY4Ut0/t35/VOYViJXb+zISafc54vG6nSZqdjbbh4f3F89vTKg7yMaZzuOC2I8aMQB4bAh8DTI/D3of8uGTP90X32eS3gdj9pX2adu9e0/ln6uZm3vamE3a0vOskgHH3jsiOz9/ZNtx2NY0O05FPzhTj3so8EbL4z5TIZxMAHC4+g7sD5uXsXuIg+VDzBrlMf+N9sfOQQ81ul2eK7zpeAwjkuh3/zpwewzuNvtERQgghhBBCNA5NdIQQQgghhBCNQxMdIYQQQgghROPQREcIIYQQQgjROIaWjGBro4WlM05t5cRmY5O5sGzJmTExYVskQcEEEVt6oSYzrouY0DExrRdaMWEfE2h54y5m5OXLVlfydqjb2CATBtSWVKAqkcQDQK62Y50sFwDy4/m2WZ1IogOR0d7CrsNFJXBmiDtHxIwRQ9yqz27EYJAalBY7MTtPkgFMFMsiSQU69a480QAz0PTmyqwsatjpiYieqwrfmVjZnx87X38tD62ezupMfpkc8F63ne+WC9jZc+KFwgDMPasHW3nCgouz5clslsm99J9F7LOpatKIUSBhV5YIqD3jn908KUwWvZnpYsWcDV6LP7GZNz7Typ9d/34wh4ezOsvus4glS2Imj1XGIUuIsdzK32s2WuVtR4w+I7Dz8LGKvVeycTE3W4yfpw/n13Kr7dw52ecO+Ww4fHUxCQq7l77fzIgeh8jxnuC2fVwCclPRI6QOiVWLrWLcGbQBsb7REUIIIYQQQjQOTXSEEEIIIYQQjUMTHSGEEEIIIUTj0ERHCCGEEEII0TiGlowAGwaccdK9dnF7fTJXZa5PFgWAF73gGMD4JHG/ninW8466QO5yy8RnTMzqRZkRgZx3fQViYr++uk8P72noUJfomxJJDsAOyJIDRJIRsE7547FkCBUssgHk/R72zRwsrbHNTLS/OVcU5rIkHZsbLkkIS9rBjueUwa12/qBOzRSfk6lWLvpmwnefDIAlDMiTCuSiVJaMINK27xMTD0cTtZTB4tkqEfh6cTTbz8dUFk95TPfu6oE6K1tZHSoo9k0xcbp/dFg7rMxdbmIcX8kBnsH2Y59hTWELlj2H463iM9iazK9Je7P4XFj0cgeS9/jkE7tbeTKEjdk8nkTGjq/D7i0bl74t/l4TSS7CPq+Kx4s8b5G4ALBkC/nAzJNM5TfTJ7miEOH/8lzxfZR97rBEW0dQTEawJ8umk8MSkKxck9ebjMQh/0q+P69y7mD+3u77cJG8j/nEDv6ZSCDJhILoGx0hhBBCCCFE49BERwghhBBCCNE4NNERQgghhBBCNI4hanSAzOPOL+1jvZssaia2JnMNxcoMWeO9UlyTOTWTr63cnC4ekJlBMY2OX/fN6kSoS3/TZou1aZnXSLHGSrYvVdZPqvRpg2ltmEanrCEgptFhx/NtMd2Q15xVNRBtrpkfo4UNzLUWfGGR3KMs3LbHa1SYgeZu95xE9DgAMO8CY0SjwwxD95H9ZgLmmF6ryDQrTIeYa2TK18azdf/M4M6bHrLY7E3o6tQz+n4vz+Tjcmw210ww87wMf3nZc3qAlLn18YuzeZ/8tWTXjWkYIvey2Vipyfdmm10Tot3ysNAc0XK5sjHykb5vkwgr9hdjA9fFXbmOh5UxQ/PIM8g0G16jwzXMxbJ+mtgyXSIr87D3wbUJdw3ImGf6n0POcZhpMz1Mo3Nq+nDe9vXFtpkOcdPdgsXp3NV0AXOlfWDPUj9jjr7REUIIIYQQQjQOTXSEEEIIIYQQjUMTHSGEEEIIIUTj0ERHCCGEEEII0TiGl4xgE8i8jryOjiYjcNu5FooKz7dmio0FbJ6A6bwoYpzWIiKq3HyqmmiOCYMzkeRkfvyxyVwQlxmy5j5PeRmr088EBVXb8f2kvlrMsNPDkgGEnp5AW5GEBVGIEHoH0cZmqTCTCej9uIwkGwFyoSgzbvNC/0jiASCWaMC3FTEHBYA9q8U+TV3IBafemJB6TDKdqHt014nA1ov4F1v5GLxIkn34+8IEtv5ecoPD8vHFhLL+eGdaeXaA1rHTWdn0pLu+LKlA5HOP7HfhYPH/lKxPDzthMBOLs/ONXKdInSbT2sgHhgWMP2migciY85DPNJvNy/ZvuMYPPpTV8e8QLGEAG3M+xrGkLFWTM/n3qrVVZvjs3r1I0qW1dv58T7WuXOge+fwA8mQujMj7IGvHfxawzx3fFkv0cBbzWdnmhEvsMFH+jhpJHAOwZATVYnNV9I2OEEIIIYQQonFooiOEEEIIIYRoHJroCCGEEEIIIRrHcDU6fgm57w3Tg0SkLaxO2wqbW21inDZZXDy7uZmv49wMrO1k6zb9On9WB2Qtq187y/bL1o6SLm7O5IXnV9w1WCEXvC6NTtU6ESo/xUbKmHFZ2QGrmpRV7XhdpmjN0fXswla2XtmvVWbmbl7zxuow47aI/sYbdnrtTacs1994vU1kv33ncqNAy3cDzrttpheIPF7s0XVL4ceIXmDsQvGZax3Ir1trIo9xmQ4xYCbH6kRMB7l5YTlrE7kWYO6a4vmNX5Nf8InV4jO36s0EwdfCZ7ohIuTx5n1MZ8HO1187Ziq608h0FRvEHNTf3gukoUgZ8f3MxiX7LGZtuz7tJ42vHSwGi4jOAsifL6YhjuiRmU5sebXYh4tLeZ+8RofBdDv+fWhjgsWKokaG63HyzwZfj2k8vW6JG06Xf+6wtj0sdjBTT69pZJ+FkbjAjWWLbXuDWta23070fS2GvtERQgghhBBCNI7QRMfMbjKzL5rZCTN71SXqvMDMPm9mnzOzt9fbTSFEU1A8EULUgWKJEKKM0rUzZtYC8HoA3wfgJIB7zOyOlNLne+pcD+CfAfiulNLDZnawXx0WQowuiidCiDpQLBFCRIh8o/N0ACdSSvemlNYAvBPALa7OTwB4fUrpYQBIKeWGAkIIoXgihKgHxRIhRCkRNfTVAO7v2T4J4BmuzjcDgJl9DB0p/GtSSn/kGzKzWwHcCgDYdyw3vOqnxtvr7yaJKHWj/HLwRANrbrtcfMZEexGY+RYzX8og5n2bc0Wx1xIT9q04U0tmvBkxGh102otIogP6fDETz7LGWUN1Cf37mTCgrqQGV0Rf4snuY/OZUDOSjMALPplJm2+XlVU19TyI3LzP1zt4/lxWZ8wXsdc3n3gAiImeI7Ax7w2WWb4VVzbdzgXdmwdyEe5qy4tZ85jny3jCF9al8kQHPmFB1FAxF2sT80QXm9nx2fl6kTFPNFCss0wSD3DD0JFIPlBbLOnW+UY8OXJsVxY/sngSMf5kyQHYuKwrSQgxOY+8ahyYKHbg4mz+nDABu4+fLMZ6mICdPd8++cD6AjH4XikXqG+Rz36fiMm/CwHIriVLqsDK8ne9/Jr45AMs8QD7LPJlETNWJvxnySb8mF8OxE8WJ9j99fFz0PEl8vrJnqRE2rkewLMAHAXw52b25JRS4ZM+pXQbgNsAwI7d6NsQQjSfvsSTuRu/SfFEiJ1FbbEEKMaTp9w4pngiREOILF07CeCanu2jAE6ROn+YUlpPKX0ZwBfRCS5CCNGL4okQog4US4QQpUQmOvcAuN7MrjWzcQAvBHCHq/M+AH8fAMxsHp2vi++ts6NCiEageCKEqAPFEiFEKaUTnZTSBoBXAvgQgC8AeHdK6XNm9lozu7lb7UMAzprZ5wF8GMAvppSYXZ0QYgejeCKEqAPFEiFEhJBEPKV0J4A7Xdmre35PAH6++xNjE7mwnQlcy4gkHqjYdquVi7FYEgFftpuIyHyCAiYiizgIe7deIHe2jopwN6aLgrDVlVy0tj7jxPkzpCF2bf19idRpFCypQV2JBaomEehnYoM4/YgnhlQ6xiLu00wA6pMKdMoeLq3jkwocIMkIWIKCAxeKmQbGWKIBn4yAvbpFkhFEQgXTjUb2IwlQsjLyKLc28sbbLha3gzEuQsTt24tpfcy9VJ+8gJvFZh/3/bFYH4FcwM0c0H2daOIBfw1Yn7YDfXk3AbALKU8ytFrcNpZowJexMcjG6iNumyX98a8e7JawMRdIRjDm9tszm3eAJWXx1yjy7sGepeXV/NnNkg8sEEmW7yb7aGTvHivFQpqIydGeLk9EBfB3NI+/TqwdntSqWMbeGSPCfxa/fL3oe2RZO/3eL0LIMFQIIYQQQgghRglNdIQQQgghhBCNQxMdIYQQQgghROMY3sLbhFxLE5Ee+B7XpMcBgFb78gZhwKXWUpZrAXKDqHwdZ0yjk98yv5YzYtoF5Ouw1+byBb7nltxazkmiPYnodtiTFjH1jMAu21C8MOsg0vGqWpvtodHpB7uwVarJ4cZty267fOzy/fLxnOt/yjVCADDp1/lHjAlZnYBhZ+hxiy6djtSrOMZ9rGI6Gg83/izXo8R0LAGT5hqpqqOJmKFGNEk7DUPK48kFZ24bMQNlGp3c/zfX7bC2Ix7jFTU6mC1uzp3PNTq7Z/NY5a8R03VEzHa9OSiAXJOTezLHNDrsUfbvLBv5e82S19e1iWZ7otwMtKr2hF3LKtrEiAEywCRgTE84WN1OXegbHSGEEEIIIUTj0ERHCCGEEEII0Tg00RFCCCGEEEI0Dk10hBBCCCGEEI1jeIrDiGFoRMDOEg9UFKe32+UmTswwNCI6nnFmW0y8zJIReEEYE476ZARcEFhuOLfWypWMizNF067MQBTg96DKvewn4eQEqaYDMuF/XYkGqiYVGNkMDaV0xMOXHwfMgM3X4Ua+5aLMyNhlMFFockUWGTtMhBy53RHxLtORTpMyL/BldVzZOqlzcSIXJvsEAVx4XyzjIvu8zLfNxNKDNNCMCncj9WJJG3Z24gGGIWFisxgvzCcAYaaekWQEzDDUJyhg+1VNRuDDEKuzv7g55g1MAUzNlhuhR7hIjG3XfdIjAJkHM0tG4OuEDUN53wq0i+86F2fyfu+eyN/1Vt0F9u9ZwGDF+dGkLE1G3+gIIYQQQgghGocmOkIIIYQQQojGoYmOEEIIIYQQonFsL8NQD+udX29Zo+zAG0JFDZu8loeZgUaMCbk+oHiCbD11TAvANDrFtaTLZO3s1Eyxn+cn9+aNszWwdZmBMqpqDyo1HtHaRPU4delvqp5ccw1DDSkbB1XMzaLrmX0ZM5n0a9GZOehF5GvTF2eL6973bpD75rsUNQr0ZVU1OmzMe73NbF5l3YWPxdm8oUXiQOyvE7tuy66M3RMf84D8XkYMNOtc9x7R0VRF+ptq7NpKGF9x486beDJTT1/GtDZE/5LpdpipaERXEtEsM+2c7yc5N6ZPjsRYPy6YZgVLRPvrNVBej8PKooah/loG3jXXnV4ZANbmFvOyVvH8qhr5bkd8rKpiYDoM9I2OEEIIIYQQonFooiOEEEIIIYRoHJroCCGEEEIIIRqHJjpCCCGEEEKIxjE8peIWqiUj8PtUNAeNwEwAWcIAL9JjJlo++QATJjMTUQ8TrbF+epgI1/eJntuEK5skwug2ERIO2ww09Awwc1B/fpGkAtHEA3UlGuhnwoKdRUQUysSzXgwfMRXlx88HijfuvXgwF7xOHXDxZCl/JsYiyQgiEJ1sIqLnVRdiLk6zRANFQS9LgOLrAHliB594gNVhxoTMDNTfX3pPmIDaUTWpwKgmDIh87owqtgVM+LFStg3kIn6WeIAlKIgYjbLkBx6WlMQ/lsyw1PeTmKEyA/UqyQjo887eBX2igUgyAtYOS9BQ1g6QGyAvWVblIjE6XZ29cgPiqLlxXfD3yO2VWMD3xx6Dmbu+0RFCCCGEEEI0Dk10hBBCCCGEEI1DEx0hhBBCCCFE49BERwghhBBCCNE4Rj8ZAWujjwkKGBFHdl/GhH0siYEnInhdC7ad94nVcReuzQRrJBlBXUTuZeX7HdmRCf8jdaruF/Ibt0IAACAASURBVKGuhAXNJuJ438pcu5maN3KscgG7T2AAcOF9KHFJq5i4ZGI25lruyyKC8oizN5CL+tfItYzUYdfJ12MJInwdnnggP14kIUUk7o5qUgFGkxMNRDF/CSLvHn4YsgQCrMwnA6gzGYEX47N2fPIB8iriky4B+XPCnhs/dtZWSWIPdi19GUmQkJVF3wf9UGUJCwJtr63kF3xt1ic3YYkGqsWKqglPthuDTnygb3SEEEIIIYQQjUMTHSGEEEIIIUTj0ERHCCGEEEII0TiGq9Fhay57qarRiazT3MjNnzY33PrHasv1Kfna+NgaxZghV3kdvl6/fB221x/1lajWJqLRyYiYg7Iy1rjXTETXs0faLtsnys5aY59gpeuXmR7Er5Vm66kjWg+mtfE6PLbGnWn1fD02Br25b3R8R/SEnqhxnb/+EW0PX79erpGp2vZO09p4pL0JkpCHUD9UmKQ2YmgeeWeJmP1Gb2WVtkmd9mYeK9qtmt4Pqn7O13W9Kx5/y78zopoZ6KBjzqA1MsM2I9U3OkIIIYQQQojGoYmOEEIIIYQQonFooiOEEEIIIYRoHJroCCGEEEIIIRrH9jIM9b2pMxlBYL9VZ/60Nl1uLgcwoWxEfFZNKBsRtjEiplWs7ayMiO9qM/XsazKCqo3nZo3VTEUZkUQDVcXDO8swNMFKBZ3s+fbifJZ4gBERV8bq1JMQpM7kJnXs09mvf8L3gSZJQX1i2sGLgJtzDwZKQp58IPK5U7bPY9gvlRmsg5icArEkBoFza22QhCd1JSNgVPmcjz7uld4hxKiOeX2jI4QQQgghhGgcmugIIYQQQgghGocmOkIIIYQQQojGMTyNTgLX0vRSp0bHm5MSs9L1leL6/FWyXv8iprKyZVe2Shq/iN2F7ehabW8GyrQ2vk/+WJ0+5Xojr0dghoprm65sZSzvZMSkq6JuKrSeOLB2OWYOyspYB6rUYcgwtJ+w5zmvUyRqYOnh2rlyM9LtaGrpdR1sXTY3KC2WTRDXQV/HG5+y43faKjdI9ceL9JGVseP7exK9Jh52v6WBEhHMDfkUvf0DfMtjz03lZymi2S7bJ1pW8RrtapefbyRWRMfyKIzLYZuDMvSNjhBCCCGEEKJxhCY6ZnaTmX3RzE6Y2avI34+Z2YfN7K/N7DNm9tz6uyqEaAKKJ0KIOlAsEUKUUTrRMbMWgNcDeA6AGwC8yMxucNX+OYB3p5SeCuCFAP5d3R0VQow+iidCiDpQLBFCRIh8o/N0ACdSSvemlNYAvBPALa5OArC3+/ssgFP1dVEI0SAUT4QQdaBYIoQoJSLBuhrA/T3bJwE8w9V5DYD/Ymb/GMA0gGezhszsVgC3AgDGjtGEAAXY332PJ4P7BZIRYKnY2NKFPVmVqenlvMyZSu4mJpMRsVlEqMqTERSTDywi7/ciZkhZsR5LtHBxySU2iFxboL5kBNGyjEjCgPxe5vtFzEBlGHoF9CWeTB07UGrKGxH+swQGbD+f3IMZjfrxxOpEkoTQOqvFOpvMyLciLSew9dsAMN7KEw1MufHEkhH42Oj36ZTl8dPv55MTROuMkz7liQ7qTBhQl9FoPYkHRkHMfAXUFkuAYjw5djWpEBGw+0cnKo6vsJ9PTlBrn2pMYJAlIJnIxyV9j/NlrI5/rWHvBlXbDtRhsTFPNMDqVEv44qnXTPrKY8x2TDzAiHyjY6Qsue0XAbg9pXQUwHMBvNXMsrZTSrellG5MKd2I9lVX3lshxKjTl3gyeVU+kRdCNJraYglQjCdX7a+5p0KIoRGZ6JwEcE3P9lHkX/++DMC7ASCl9HF05r7zdXRQCNEoFE+EEHWgWCKEKCUy0bkHwPVmdq2ZjaMj6LvD1fkqgO8FADN7EjrB5Ot1dlQI0QgUT4QQdaBYIoQopXSik1LaAPBKAB8C8AV0Mph8zsxea2Y3d6v9AoCfMLNPA3gHgJemlPxXyEKIHY7iiRCiDhRLhBARQpKzlNKdAO50Za/u+f3zAL7rio6cEBSVO+pKRrBA6pxxu8zkov7dJBmBF90yt28PczZfJskAIskIvFiZJR5YwD5SNlfc3pzL6qyccfux68bKQskf/MFIndqSEUQSD7B6TKAXSXRQV6KBOpMK9M9J/UroRzxJsNLkA1WF/8su2QerxxKA+CQhLLnJxaV8zG/5siUiRYiMnaq3u4pQFwDmis/q2Ew+5vbMLRa3W4t5HZSXsTqr7l7yRAflSRtYEgNPXckB+k1dyQe2q+i4L+8mQEf94x8Vv52HinxcsDrTgbI8Z0aMyPFYHV9Ghslmu3zscOF9sYwlKSGvLHlZ/nqSxzgWl9hbrm8rcnxSZ2IyPxef8IRdEz8uI9ftUmV5nfLYVFeigzrpZ4wJGYYKIYQQQgghxCihiY4QQgghhBCicWiiI4QQQgghhGgcNdpCXSGbyNeZR5Y9RzQ6TDNSxXzqwbzS2faBrKx1qLzjXgvA9DhM2+PXWzJtj2+bG4bmZQ97jc4Zsgj2jNMHRDU6vqyqRoftl13uiNYmaupZxQy0n4ahjMaZiPYFbwYa0bcxPQ4z0vXjaXEzH19+PG0tkMX5Z/KibOxENHCVtWwEH2Ii69cBYG6ssLk+P5ZVOTfvTIrnH87qrM7mxqoR81dP1IQvsjbc63ZYHK7LvC+q/2m6/maoGMrfNdg7hNe6RPQ4ADDrttktibytRTQ6/lhA6Nw2WtU0Ol6TwwzVMUM+m1w8KTWYB/hHI7tuEf1PpuPJAyrTbHttIDNA9u96VTU6VfU/VanPAHmwMUff6AghhBBCCCEahyY6QgghhBBCiMahiY4QQgghhBCicWiiI4QQQgghhGgcw0tGsIWYuMzje8zOIFJWsc5WO1cSnsahwvbafK4IXG2VGwx6o6lOF4qiLS+wBoA1p0Bk4mlmBpqJpU8GxNJMPF2XoJo9DyFBNTMD9apEVidiIjpsw1BGXUkFdlZyAiZgj5iKsvHkyxYX8vGcJR94kHQqMp4i4yuajCDyyHkhMk08ECijfSomN1nZ2J9VYaGiPVsusK0q8KUGhhVgsbku0e0oJB5oUlKDZMC6CwVjPjRUFf5fIGWRR9C3zcYy65PPn8T65MtIOywBh4cl0vDjkhn5Ts7lBsAr8y42sPP1sYrFnEjiqXlSx8WzGdJHZlzskw+wJFP+XY/FIHYtfdmojLlh91Pf6AghhBBCCCEahyY6QgghhBBCiMahiY4QQgghhBCicQxXo1Omv4iaP0XqRDQ6EUiftlaKi2fPLRGDwbniGv6pGWIiNVFuGMpY23QanaXc9HDlzL58R28GyjQEvqyqzqCqRoeevteWMK2Jv76sTsTok+l4qrQT3a8qO0tvEyGypjxiRMnK1laLppbrZMyFdGqsrIpGh7UT0eiwSxTR6FRtO2D4vDJDNFEzxXG4u5XHT2+cHL2XubEs63gemz1VdTRRg9DydqS/qYu0C1ibLP4feGxyq1gpYvzJtDcRPQ7T2jBtj4fpUXyf9pI6/lzIuVU16fWaFaZrmZvNA9iDh30cICfni2o0DJ08fK5YZTrvIzsXb4jKDEO9ATHT8fg6ADMDZTqecj0j268uo9HtGCv0jY4QQgghhBCicWiiI4QQQgghhGgcmugIIYQQQgghGocmOkIIIYQQQojGMbxkBAm5oNXroyIazao6TrZf1eP581iyrMr6XFEBeH6SKAInU17WdgfcILdswx2Pifqrip59WZ3JCHwZNZAl1wSPuO2IGWjEVJTVq2r8WTU5QF1JBeoROI8yueBynNYrg4raN1wZHZcl20DMJJfVqWq2G0kYEDHpjSQxiPSJmormSmx/vTdbw/vo6jfRpAZ1iX63o3h42GyZYXWiGC+mposPqzHjTZ9ogCUeqGLaC+Rjhd02lsTA99MbiLI6JBnBaiB+Rox8mTh/H3lBWJ4vJiM4zzIGzLgLFU1GMFn8nGWGpT5BAks8ECnzyQkAZirKDEPLDY9ZrBjkeB6V2KFvdIQQQgghhBCNQxMdIYQQQgghROPQREcIIYQQQgjRODTREUIIIYQQQjSO7aXo9EIyKk7v07FYWVQ87MuIZi5z4mViw8k8iQEwRsocEYFvJBlA1YQFkQQFIVd4lnggF/vFEg34skjiASBPBhBRjrIEAlWTCvQziUBdiQ62H4ZUSRgZc5Huo+By2BE4It7dYXktmGt49bbKncwjyQdGwaW8zus2bBJ2Yc0p+y9OF0X10ytb+Y5e1B+9JH7MXSB1IklC2HuFTyywn9RxZeskGYG/HgwmqvfCeybgnyMvCKsTxeONX72W1Vm+sLuwveGTxABot0mChEmXIKGVJwzwfYomI5hy7xV+G8gTNEwgP7cJci399Y2M72jMibU1GskHPPpGRwghhBBCCNE4NNERQgghhBBCNA5NdIQQQgghhBCNY4grxLeAzEjJ61GIPsXrYaLGnxH9TUTrEtHoeD0OK2NradndiKyX92XRfkc0OlV0PNH9Mpgex5uDAtXMQCN6HLZfRNcS1b7UtYa9uVqbx4JfP+zXPW+SAbbq1p2z9czUBM+t8cYkcQacdIOc6vICZZE6LOYwIoahkbar9tsfj7ZDzPPa5VqXqnqrKhqZ6HNShe2gx2mS3qYKCZYZZLYmigaW49P5h+pY5BawMeflL0yjE7klrG2vtwkYhi7P5O9e1DjZlbGx5A0zZ4IaHd8206ysThcvHOsjGxe+LW7qWXxniGqLfD1mkBoxDPU6HiC/vgPXlA4QH4OMarhj6BsdIYQQQgghROPQREcIIYQQQgjRODTREUIIIYQQQjQOTXSEEEIIIYQQjWPIyQjKTB2Z6LpoEIUVYrIZSTRQZzICr0eLJCOIJB5gZVWTEUTOhSUMiNSJlmX4RAMR409WFqkTSTzA6kWE/1WFu3UmFdjZ4uGOYWjxGrScMJUJrL0odc0JkFkdABhvFcsmZ3LB6cqMU9pH4gKQGw5XFSEzoX8kGYEvi/SRlUXqkLbHfKIH5IZ+zGDPi3dpEgmyn68XMdOLJDDo7Fe84NH9yo5flZ2eZCBKgmUGmf7eLc7mA2yP+6Cldt9VkxFEHoFci58nI/CmpgCSK1tt5eagPjkDPzwT/hfHHBP1x8xI87HL4nUE3xZLBpAnFcjfMyImoizRQcQwtHrClfKYM2xz0EHHIX2jI4QQQgghhGgcmugIIYQQQgghGocmOkIIIYQQQojGMUSNziZyjYZf0er0OADyReakzkZFo9Eqxp9ANYO9qEYnQlWNji+LmKEy7Q1rOzN3ipiB1qnR8fqXiB4nul+knaoMcu1qc4xHDYmuc+6FG94VBx1bB87WWHuj0bWZfI352lxxof3Whl8sj+q328cKNnbpuAwQMf5k+pv5km2231zeyT1zeazw94CZ8HktFdNWsbXhfn1+ZN17nUZ9kXX3sXYGu+69qt5oFNiCVdN/OK3LVDt/vieZHMWXsXeIqlo93zYJQ4uzxXemi+S9ihkue9iz68cq07qs0feDIkxHE9H2RNpinx15zMn7zeJQZD8fm9i5cd1OuXGxdIA5+kZHCCGEEEII0ThKJzpm9iYzO21mf3OJv5uZ/a6ZnTCzz5jZ0+rvphCiCSieCCHqQvFECFFG5Bud2wHcdJm/PwfA9d2fWwH8+8feLSFEQ7kdiidCiHq4HYonQojLUDrRSSl9BMC5y1S5BcBbUoe7AcyZ2ePq6qAQojkonggh6kLxRAhRRh3JCK4GcH/P9slu2dd8RTO7FZ3/qgDAKvAE+nXzQIiI87np5TyAM3V3ZwCo34NlVPv9LUM+fuV4cpv9k+HFk+qM6nPSt36zt9bLvcleIbreg2Vk48mT7d5RiyeP4RnxiWm+Tuqwsi9UO1yRUX221e/BUjmW1DHRMVLmU251ClO6DcBtAGBmn0wp3VjD8QeK+j1Y1O/BYmafHHYXSJniyTZD/R4so9zvYXeBlDUynoxinwH1e9CMcr+r7ltH1rWTAK7p2T4K4FQN7Qohdh6KJ0KIulA8EWKHU8dE5w4A/7Cb3eSZAM6nlLKvhYUQIoDiiRCiLhRPhNjhlC5dM7N3AHgWgHkzOwngX6Dr7JlSegOAOwE8F8AJABcB/Hjw2LdV6O92QP0eLOr3YOlrvxVPMtTvwaJ+DxbFk8Exin0G1O9Bs+P6bSnR5apCCCGEEEIIMbLUsXRNCCGEEEIIIbYVmugIIYQQQgghGkffJzpmdpOZfdHMTpjZq8jfJ8zsXd2/f8LMjve7TxEC/f55M/u8mX3GzP7UzB4/jH56yvrdU++HzCyZ2bZIMxjpt5m9oHvNP2dmbx90HxmB5+SYmX3YzP66+6w8dxj9dH16k5mdNjPqE9EV7v5u95w+Y2ZPG3QfL8UoxhPFksGiWDJYRjWejGIsARRPBo3iyeDoWyxJKfXtB0ALwJcAPAHAOIBPA7jB1fk/Abyh+/sLAbyrn32qsd9/H8BU9/dXjEq/u/X2APgIgLsB3DgK/QZwPYC/BrCvu31wRPp9G4BXdH+/AcB926Df3w3gaQD+5hJ/fy6AD6LjQfFMAJ8Ydp+v4Hpvq3iiWLL9+q1YUnvfRy6ejGIsuYJ+K54M9norntTX777Ekn5/o/N0ACdSSvemlNYAvBPALa7OLQDe3P39vQC+18yYydcgKe13SunDKaWL3c270cnPP2wi1xsAfhXAbwJYGWTnLkOk3z8B4PUppYcBIKV0esB9ZET6nQDs7f4+i23g4ZBS+ggubzZ/C4C3pA53A5gzs8cNpneXZRTjiWLJYFEsGTAjGk9GMZYAiieDRvFkgPQrlvR7onM1gPt7tk92y2idlNIGgPMADvS5X2VE+t3Ly9CZZQ6b0n6b2VMBXJNS+sAgO1ZC5Hp/M4BvNrOPmdndZnbTwHp3aSL9fg2Al1gn9emdAP7xYLr2mLjS539QjGI8USwZLIol24/tGE9GMZYAiieDRvFke1EplpT66DxG2H8/fD7rSJ1BE+6Tmb0EwI0AvqevPYpx2X6b2S4AvwPgpYPqUJDI9W6j8xXxs9D5D9Wfm9mTU0oLfe7b5Yj0+0UAbk8p/Wsz+04Ab+32e6v/3avMdhyTwGjGE8WSwaJYsv3YbmMSGM1YAiieDBrFk+1FpTHZ7290TgK4pmf7KPKvx75Rx8za6HyFdrmvrgZBpN8ws2cD+BUAN6eUVgfUt8tR1u89AJ4M4C4zuw+dNY53bAPRX/Q5+cOU0npK6csAvohOcBkmkX6/DMC7ASCl9HEAkwDmB9K76oSe/yEwivFEsWSwKJZsP7ZjPBnFWAIongwaxZPtRbVY0mdhURvAvQCuxd8Jor7V1flpFAV/7+5nn2rs91PREXtdP+z+Xkm/Xf27sD0Ef5HrfROAN3d/n0fn68sDI9DvDwJ4aff3J3UHpW2Da34clxb8PQ9Fwd9fDLu/V3C9t1U8USzZfv1WLOlL/0cqnoxiLLmCfiueDPZ6K57U2/faY8kgOv1cAP+9O/B+pVv2WnT+0wB0ZpHvAXACwF8AeMKwL3Sw338C4CEAn+r+3DHsPkf67epui2ASvN4G4HUAPg/gswBeOOw+B/t9A4CPdQPNpwD8b9ugz+8A8DUA6+j8h+RlAH4KwE/1XOvXd8/ps9vlGQle720XTxRLtle/FUtq7/dIxpNRjCXBfiueDPZ6K57U1+e+xBLr7iyEEEIIIYQQjaHvhqFCCCGEEEIIMWg00RFCCCGEEEI0Dk10hBBCCCGEEI1DEx0hhBBCCCFE49BERwghhBBCCNE4NNERQgghhBBCNA5NdIQQQgghhBCNQxMdIYQQQgghROPQREcIIYQQQgjRODTREUIIIYQQQjQOTXSEEEIIIYQQjUMTHSGEEEIIIUTj0ERHCCGEEEII0Tg00RFCCCGEEEI0Dk10hBBCCCGEEI1DEx0hhBBCCCFE49BERwghhBBCCNE4NNERQgghhBBCNA5NdIQQQgghhBCNQxMdIYQQQgghROPQREcIIYQQQgjRODTREUIIIYQQQjQOTXSEEEIIIYQQjUMTHSGEEEIIIUTj0ERHCCGEEEII0Tg00RFCCCGEEEI0Dk10hBBCCCGEEI1DEx0hhBBCCCFE49BERwghhBBCCNE4NNERQgghhBBCNA5NdIQQQgghhBCNQxMdIYQQQgghROPQREcIIYQQQgjRODTREUIIIYQQQjQOTXSEEEIIIYQQjUMTnQZjZsfNLJlZu7v9QTP7sQEc9zVm9rY+tHu7mf1a3e0K0USaNv7raLuf18DMlszsCf1oW4hho3hC91c8GQE00RkyZnafmS13H+qHzOw/mNlMP46VUnpOSunNwT49ux99EEL8HRr/gyV6Dcows7vM7OWu7ZmU0r2PtW0hqqJ4MlgUT0YDTXS2B9+fUpoB8DQA3wHgn/sK1kH3S4jmofHfZ3T9xA5C8aTP6PqNFrpR24iU0gMAPgjgycA3Zvm/bmYfA3ARwBPMbNbMft/MvmZmD5jZr5lZq1u/ZWa/bWZnzOxeAM/rbd//18DMfsLMvmBmi2b2eTN7mpm9FcAxAO/v/lfol7p1n2lm/83MFszs02b2rJ52rjWz/9pt548BzF/qHLvHe37Pdrvb36d1t99jZg+a2Xkz+4iZfesl2nmpmX3UlSUzu677+0T3Wny1+5+tN5jZ7u7f5s3sA91zOWdmf66gJYbNDhn//5eZ3W1/t/zlFWb2OTObvET9y7Zd0i92/e4ys5d348OCmT25p/5V1vlv+EEz29eNEV83s4e7vx/t1vt1AP8LgN/rXqPf65YnM7uu26cHH70v3b/9gJl9pvv7LjN7lZl9yczOmtm7zWx/92+TZva2bvmCmd1jZocudT2FuBQ7JJ78jZl9f8/2WLe///Ml6iue7MR4klLSzxB/ANwH4Nnd368B8DkAv9rdvgvAVwF8K4A2gDEA7wPwRgDTAA4C+AsAP9mt/1MA/rbbzn4AHwaQALR72nt59/cfBvAAOv/xMQDXAXi871N3+2oAZwE8F53J8fd1t6/q/v3jAF4HYALAdwNYBPC2S5zvqwH8x57t5wH4257tfwRgT7etfwPgUz1/ux3Ar3V/fymAj7q2E4Drur//GwB3dK/DHgDvB/Ab3b/9BoA3dK/nGDpBxob9LOhn5/3swPG/C8BHALwGwPUAHgbw1Mtcn0u2HegXu3691+BNAH6951g/DeCPur8fAPCDAKa68eM9AN7XU/cb7fSU9cafLwH4vp6/vQfAq7q//xMAdwM42j2vNwJ4R/dvP4lOrJoC0ALw7QD2Dvs51c9o/GDnxZNfAvCunu1bAHz2MtdH8WQHxpOhd2Cn/3SDwBKABQBfwf/f3vsHWXLd133n7ptfOz92BrsL7BLYXQIUQcoQ7YgSipTjVESVGBukIyKVyA6p0IpimogU01KVJMd05KJkOnY5cjlKVEVFhm0VJTokRcmWhKhAU5FERg4t0KRMiTbBQNmAkLAEAXAXmMXO7s7vmz/eAzh97pnp7zb6vTev93yqtmr7zu3b93X3/fbt9+75HuBnABwd/O1TAN6/p+4pABsv/n1Q9g4Anxz8/7cBfP+ev/3ZAwLTJwD80AF92huY/gaAD1GdTwD4r9H/tmYbwMKev334gMD06kFwmR9s/+8A3rdP3ZVB/5cH2x9E4EUH/UB7FcA37Pnbnwbw5cH/3w/g114MIv7nf+P6d7ON/8Hf7wTwHIAvAfibB9Q7sO2D+qXOnzgHbwbw+J6/fRrA9+7Tl28G8LxqZ0/Z3onJ/wjg5wb/XxrEo1cOtr8E4Dv37PcKAFvoT57+MoB/DeBPjfve9L/J+3ezxRMAt6M/nzg22P5lAP/9PnUdT27Sf1Mwh4H/LOf8m/v87ck9/38l+t8ifDWl9GLZkT11bqf6f3TAMc+i/y1BhFcC+At7fyIe9OOTg2M+n3O+Ssc9qxrKOZ9PKX0JwHellP4PAG8D8Hqg/1M5gL+L/rdDtwLYHex2EsDlYF8x2HcewO/tOU8J/W80AOAfoP+N8m8M/v5gzvnv30D7xrTJTTP+ASDn/ERK6ZPof3P6gRfLU0o/C+Cdg82/h/5E66C2D+rXi+w9H8xvAziaUnojgKfRn3z8yqAv8wB+CsB9AG4Z1F9KKfVyzjsHtPkiHwbwr1NKPwDgPwfwb3POL16PVwL4lZTS7p76O+hPPD80+HwfTSmtAPhnAH4s57wVOKYxwE0UT3LOTw2Wkv0XKaVfAfAWAD8EOJ7A8eQl/KJz+Ml7/v8k+t/AnMw5b4u6X0U1IJw7oN0nAXxD4Jgv1v1QzvndXDGl9EoAt6SUFvYEkHOijb18BP1vjo4AeDTnfH5Q/j3o//T8ZvS/BVpGf2lLEm1cRf9l5sV+nN7zt4sArgP4ptxfp1z9cDlfAfAjAH4k9TVAn0wpfTbn/FsH9NmYcdC58Z9Seiv6v7D+FvpfOvy3AJBz/n70l8tE2963Xwd8lq//IefdlNLH0I9FzwD49UFsAPrx4bUA3phzfnqw5v/z+HosOii+Ief8aErpj9CfeH0P+hOVF3kSwF/OOX96n93/NoC/nVK6E8DDAB4D8E8POp4xQToXTwD8PIC/gv589ndffOY7nrzETR9PLMCeIHLOXwXwGwD+YUrp2ECE9g0ppW8fVPkYgB9MKZ1JKd0C4L0HNPdPAPxoSulbU59XDwIB0B+ke/O3/zP0f4H5c6kvUJxLKb0ppXRm8K3C59AfSDMppf8IwHfhYD6K/s/gP4DqgF1CP/BeQv8l5u8d0MYfAPimlNI3p76Q+Sde/EPOeRfAPwbwUyml2wAgpXRHSunPDf7/nw4+bwLwAvrffkS+VTFmbHRh/KeUTqL/kP0r6C9V+a7Bi4/6vHVt79uvAz4382EA/yWA/wplLLoOYHUg7P1x2o/P0X5t/yD6WoBf2lP+swD+7ovnO/VFy/cP/v8dKaU/Ofh1+wX0l6A4NpnW6UI8GfCr6GeY+yEAv3DA53U8uUnjiV90XGUcuAAAIABJREFUJo/vBTAD4FH0f+34ZfTXZAL9yf0n0H8J+LcA/sV+jeScfwn9ZWIfRn+N66+iLzgE+mL9v5X6WTp+NOf8JPq/tPwPAL6G/jcIfx1fv3++B8Ab0V93/+M4INgMjv1V9EWB/yGAX9zzp19A/6fkrww+3yMHtPGH6GttfhPA/wvg/6YqfwPAeQCPpJReGNR77eBvdw+21wb9+Jmc86cO6rMxh4RJH/8PAvi1nPPDOedLAN4F4J+klE7sU3/ftgP9qiXn/Bn0fx2+Hf0MVS/yvwA4iv6vw48A+Je06/8K4LtTP4PST+/T/EcAvAnAb+ecL9K+D6G/dPbKoP03Dv52Gv1r+gL6a+//L/QnYMYMg0mPJ8g5XwfwzwHcdVAf69p2POkuKecDfzEzxhhjjDHmUJJSeh+A1+Sc31lb2dx0WKNjjDHGGGMmjsFSsHcB+Evj7os5nIR+kksp3ZdSeiyldD6lVKzTTH2zpF8c/P0zqS96MsaYAscTY0wbOJbc3KSU3o3+ErOP55x/Z9z9MYeT2hedgYjpA+hne7gHwDtSSvdQtXehn7bv1ein0Puf2u6oMWbycTwxxrSBY4nJOf/jnPPCIMOaMZLILzpvAHA+5/x4znkT/YxZ91Od+9FP8Qf0hU/fOchoZYwxe3E8Mca0gWOJMaaWiEbnDlRNki7g6xkdijo55+2U0mUAJ9DPMPESKaUHADwAAPML6Vtf9Y18+GpihCMitXiiMt4GgCO7IsECFSWVgyGSl0GEyExlu72y0i69U+685F35dbbF5eCySJ0tTA+t7Z1c9ntnuyzDDr1Dq4SGuzXb0TJ13dR+TGS/yD0RvZea9KlpO6pOpO0rv3cx53xr4AhNGUo8WZjHt37jq6mVzZptoJ/M/KDt/fbjMmG/tkn3vDKqaJrnk0ecCuTTYlgmDg0zYkcuU3VmA/uVYQi7VGc7lT3XsbFalsV3dBxjd0WwVvtladPVDsNse5THUs/ZSJ0/+r3nhhlPWoslAM9P8K13f2P1XuntVoNqavpsEl8vZ35c9spKW8VzvhyYG2JgblC9TVFns6hTtr29XY7VzM/+yHNeob5y57KpsqHedPWAPRFlp2TZTm2dI9Rx1TbXUWVN57GTyrDi0KUn1rB2cb1R45EXHdUwX5VIHeScH0Q/vSj+5L0z+V987lTl73wjzYoZxgyVze6UM5OZ9XLW0aN7dFpNaNRMhBFnbIvixvXFstKV3lJ1G0tFnYsoM6xewknaLus8g+p5fBa3ibZPFmVcL3L81Y2Vos6V1fKz7K4uVAtWiyr95M57WQ/UUfXUdYvUaWu/UbetzhPXi9RRbf9mOsj9ug2GEk/u/Q9S/tzHqQJ7WP+xaPXLtP24qKO8sKmt/FRZ5SvPVbefEc1cEWUReMSdEnVOLZZl06+gAmUByGV3iTqqjPe7vaxy9fbq7OXi7C1FnVWUMWYV1XrXcbSoc+3r/sEAygkeoCd5/GKlXrSawi9ow2RniLmF1CSPUc/rd6WPDDOetBZLgGo8+eZ7e/n//Fz1flq6Wn0YzV1FCc8r1GkTXxJs8eNyeb6ow8/rJytenn2+jDuLsidosD4h6nBbqu1nLpVRZuvisWqBes6rZxEzJ8oofh05WZ7w5ZPVA670yg6cLN9psUIdPYFLRZ0lis68DwDM41pRdpTK9Dy2eqPwixcA9EZsd9NW3BtWzPv79/56430jS9cuoOqOewYAP9ZfqpNSmkLf0Z4e88YY43hijGkFxxJjTC2RF53PArg7pXRXSmkGwNvRNyfay0Pou1wDwHejb2jUnd/ijDFt4XhijGkDxxJjTC21v3UP1rW+B32H3B6An8s5fzGl9H4An8s5PwTgnwL4UErpPPrflrx9mJ02xkwmjifGmDZwLDHGRAgt6s05PwzgYSp7357/rwP4Czdy4CPYLdY3sv5mfqNc/zizXhV6Tat1spEypdGJrCUVZ2yayqYXSo3QseXqr+Xrx8tfz5cWyhX787he2Z6VHa8SEfMC5Tp3XuPer1O9Btdmyzq9qXIt6e4UfWk2NURRbkRbFdXRtHX8pm1HdDTRPgxjnxYYRjzBLsoxzeNZxYXLtP2CqPNsfdkTYjEMixPKVeBao3OdtlWQppXxuh3+bECxOn9arY0nvQCWRZ1SzheKsb1tEu8KvYLSmnBMU6Jrjl9KUB3R7bSp0WmLUWp9FBENwTA1QvsxlFgCYGprF7c+RQJRHuNifAUez2UmEQDTNJ5u3SjFqZu3Ve/d54WWjecLCjUGWDN88XI5wLcucNRBf2HgXtrU6NDH213jwAQ8t1Yd8ztnxMkVMYbvXZ579suqF/Oa0AVyHUCNg7JtrqPG1zAZd4xr8nlfTsKGkGGoMcYYY4wxxkwSftExxhhjjDHGdA6/6BhjjDHGGGM6x+gX1Q5I2C3yjc9frS7mnFNrYHkNvaqjynj9uFqvz2tJo8sIeQ1ouZS0WOc+V1rd4I7byoX+M7dX13cqPwNev63WX6p1uddpTbvS6PC6VKURmp0ry7aET1AtbWpG2tLtNNXftNX2MI/fNfjzRcxAuUz5NomyFyjGqHy1rMlRUh8lCeJV9sJ3M7ASfx9tD/X7VCR+Bs9JyCcqgIpfXKb0N1wW9dHhehGtyTDXuEf9M0bZB3VO+Fk0ap3BUNlA6bHFA1oN+oiPjpof8JgTupbbZqsHvLhc6miUZoSvpRoDrNFZv1j6WxV6HAB4grZLyxodK5iARgenVdtV7e9l5Sh2piyana2eJ6XR4bmO8sNR55LrqXE6ao+cUdI0DvA54W1rdIwxxhhjjDFmD37RMcYYY4wxxnQOv+gYY4wxxhhjOodfdIwxxhhjjDGdY2zJCHo7GUuXq4q76UiigYggUDnzRYwBOUFBxOgKKIV0Smx4nLYjfURpHNa768miDgtFlUBOm4FWy1iQqMqUaE8ZhmKKVZhKUh1g1KL+cbfdpJ2mfep6cgImck6UjlKUbdF+kaZLG+HYJYjsF227qDfmeyAqqOd6EQPNiPGoqhfpU9NEABER8rjN/A5LH8bKJspkBM/Qtpp7qCRHjJofcHIicZtMU0KjleXSnVMJ5hlpGLpDz/6nhcH3E6Kx87T9tKijTEQZlYzgZKCdYo5W9vvylEja8MrqeVLzGja0PypSwHAdoDy/KkEEXwEVz7qcsEB9NicjMMYYY4wxxpgbwC86xhhjjDHGmM7hFx1jjDHGGGNM5xifYeguMM3rWVmjonQs7LqnXPhUGa+vVRqdgGmXhM+iWoPLy0RvF3XU+l5aynh8quzU5tmnKtts8glo/c0qOXKxgStQri9VhqFao0NlUwGNzmHQo0xC222ZiHZJo5NQrvNmqVopXYvVEeP52GJ1e0no61iWp3Q0Cr4sKkjzCD8m6ijL3mP8+dTaeC6L1AHKjopzuTNV/W4tqqOJaXJGpyvp8vp5xc32ebEB4HEqe4q2IxodFWOXRRnHDzXoScezdPeVoorSjDBqzF1ZZY2O2FEZhrJGR9VhbY06J4uijDU6yng08gybK+ceq4vVuc/SifJc8pxpCWWdDRHkWMsTiXHKCP4wwnE4ag4aiR+lRofPiTU6xhhjjDHGGPMSftExxhhjjDHGdA6/6BhjjDHGGGM6h190jDHGGGOMMZ1jbMkIsItSuMeCPCX240QDLBDcryySxCCSjEBpqlgDG0lGoMxQI8kPhMD31EK1sdXjZRYHTjwAAJeoU0psN0/COmkY2itPyhFKRrBb1HgZjNvUc1Lb7jJHgEzjLvE4VCLgyLgUSUKmaazeKc43J1tRCQNUThS2pVNpPDgZAfsNAsApoc0/xhVVUhTOosDbgD6XXCbi4MZs1SqvaeKBNmlLCKw+S5dF/JMioG7EBkrD0D+mbTU/iSQVUPMDfvarOjRW2XAdAGaW6w1DN4Vh6BYnI7godlSJBp6o2QZ0EoEInIwgMj9SSVLKqQ+2Tlaj8ZUTZeoWTuqkTNfVnGncSVEmwew3Ehc50YGwsA3jX3SMMcYYY4wxncMvOsYYY4wxxpjO4RcdY4wxxhhjTOfwi44xxhhjjDGmc4wvGcEO6pMRlJr65skIuEwkI7hEx3tB6KWU/JJP4jGhBTvBquMN0ZCCxXVCpJhIUH1CJCN4BqeKMhbSceIBAJiljvI2MGRRaqTppnXa2m+YbTfd7yZLWLA9lfD88WqmjuNXSb2qxlzknKgoSUlB5oU4/26KMVkkOrgiEh1sUR+mxfGPUlyYVpkOONECUGYtUMkIzgXqqDJqe12cE3YbVwLfTZFxhd3Fldt4E/ftpjQ9foRhxtOok/lNTyAZwTUxh1DjmblNJPcohNZq7NJjfVpkMplZrn8+b4hkBFijHjRNRrB2TVR6hrbV/S0C2EUOVkKOzvMjkXgAp0UZfb4rpxeLKpvL1TikkjioxCmcDGDUyQE4DkWTpExCEoOm+BcdY4wxxhhjTOfwi44xxhhjjDGmc/hFxxhjjDHGGNM5xqfRySjXzPO2Wu/aRMcDIJNG5wlh9sW7KTO/kEZHLIN+gY5/l2pImIEWJnzKvI+WwC6dLR26lhZKY6ujhRloub6XDULV2s7W1n1Hl6Y30bE0Nd6M0GbbbelobjaNDqbxLOvQzlYHxvGecJzjMaeM+pQbJ5cp80CKTUnEs2MR3ZCC+10uMdcOpSzVi3y2oEZnnfa7tFAGKzYuZlM+QK+FV2UMrzFvU+vCa/FV20q304RoO5HPF4nN7emWOrTGfwuFrvcSaXS+op7ztK3Mfl8QseJuvuRqzPF+QvM3e7beMFTeXzxlUBodVVZMNZ4Qlb5C2+q+LQ07AdL7XLyrrPI0bQf0OACKfm+ul5Ova8tV/aDW45TnMqLRiYyVw2hAPKkaP/+iY4wxxhhjjOkcftExxhhjjDHGdA6/6BhjjDHGGGM6h190jDHGGGOMMZ1jvMkIWJPGWmGVjIDLVMYAkaDgWRLyiXwFhR9WKd+HsNQUyQhEnS3anhcdOCUM9gphsBAgctmcqDO/UBp5zZPYbxalkJHFb0oMJ8umqmW7ZZcmw7Bz1G1HaCuJQofYxAyexNlKGZtRrt7+fLHfyVPVYHHsHI9UhBINRMalNCyNJCNQUZp1qmycB+jECpHkJmRWuC7qrC6UwYoTDbA5qCpTyQiuCxPRDdSb9zWliYg+IkJu2rYiYvAXESpHxcTjFj2Pm7wNbNEzmpMPsMQeKMOCSkag5hDs8X1CJVnieCJyq0Sum7wnua0yn5EW9RdmoOqscJmIsXLWxAgX1Yu036rYTX0WKttdK2POzqnqedJGxuW55KQFKokBl6l40pbwP2oO2mTMDzNhAp+j/DLa8i86xhhjjDHGmM7hFx1jjDHGGGNM5/CLjjHGGGOMMaZzHC6NDi/tU7oDXksq1qmqde8s5VHL7lmTo+pENDqq27wSXS3BPRXRJKk6dcar0GagMf3NBIg/2tTaRPbje67Ntjts6jlMNjGDJ3BnpewireleEqq7pV51sfb8baWWbek2ZbZbrTcvIsPsTnXMzayXa9N74vqSvA3bAZnH5lz5ndXGrDLerK4zj2hkWOu0X9l1akvV2SBtjVqbrvQ3vBY8at4XaTuyXp6Pz58jul9EsxNd4871ZmWM5+PVPwea0iVdz9YO8AxpYliNwttAOWdQd6Qqe45O3YnIXEA859U9wDTW6Kiy0MyKy5RGp+GsaY00OqqPao5YPGdTUaWp8SfHAR0XuE69jidKZBwexjE/TMNn/6JjjDHGGGOM6RyhF52U0n0ppcdSSudTSu8Vf//hlNKjKaUvpJR+K6X0yva7aozpAo4nxpg2cCwxxtRR+6KTUuoB+ACAtwC4B8A7Ukr3ULXPA7g35/ynAPwygJ9su6PGmMnH8cQY0waOJcaYCJFfdN4A4HzO+fGc8yaAjwK4f2+FnPMnc84vLlp/BMCZdrtpjOkIjifGmDZwLDHG1BJJRnAHgCf3bF8A8MYD6r8LwMfVH1JKDwB4AADOnQoevQFZaJhY/qbkcCwnVokH1H5cVsp7yzpKZpWFaC6xtlBpv8YsWJeCPFZQt2WyqcoidSKCRFVv1IkOxt328BlKPDl2bhnn8erK3zkBhzLEjdRRgksW/cpEHj1K9rEw2mQfTcXxTYX/bZljRogcq2m/VaIBTuKgkhqwqalqu+k5UvcX37uqTzPifo7A4+CQJhpoLZYA1XjyCpQy+4jsnucMaooTMiKPGAkHQ0fIeLJJEh4AsVkTl6lZlLJWDcya2pxXFFXqx65OSlK/H4/V+HOg/rnDjDrJVCQpjN6vLilMmTAiSqRHqnVpUppSeieAewF8u/p7zvlBAA8CwL3fmF6O0akxZjIZSjw5fe/tjifG3Fy0FkuAajx5XfL8xJiuEHnRuQDg7J7tMwCe4koppTcD+DEA355zrs9xaIy5GXE8Mca0gWOJMaaWiEbnswDuTindlVKaAfB2AA/trZBSej2AfwTgbTnnZ9vvpjGmIzieGGPawLHEGFNL7YtOznkbwHsAfALAlwB8LOf8xZTS+1NKbxtU+wcAFgH8Ukrp91NKD+3TnDHmJsbxxBjTBo4lxpgIIdVQzvlhAA9T2fv2/P/NN3zkI0Ch3eTtObEf91h8giTKWOqmEgYEmg6hZHVcptpW/S7OidKyBjreXJhc7+DbmHEL75W4MpKMIFJH0URM2lBIeZgZRjxZxxwew2sOrBMS5QZp6lrdhEi/o2JxrhfZT7mta4FrtSyynxLLK6Esn4NIv6Nu45FEA9cwX9lWIuTrVEfVi8ThqHiYEwYcxbWizjwJwdU5mQ/cy9EkHaNmKHMTALsoJfR8BSKJiRSh/SJxX5z+w3BNRkpkktZwIsdjNZrchMf8NTHbbBKHVb2dQByOxNOX0yemaYKbun3yy0hGEDIMNcYYY4wxxphJwi86xhhjjDHGmM7hFx1jjDHGGGNM5xiSZWeAI+hLBPeyULMNAMs12/uUHb9U3X5OLD88LppilB0W629UO8do+4RqXH2WyDmhsizqqHWivJZUG+XV11FGebvrVK9Nc0zWyES0NmuBOqpsmBqdYe2z336HwzB0KGxgDn+I11bKeB0wazEAZRZZ1tncEPc83d8722Uo3eIxoGBjXcVUGayOUFlP1JmdKzUyM3NVrcV8r9R1sNaDdR79svr9loQ1IrcVaQdoz8AyYvSpYiXrb65gqajDOh51PG0wWL13omvqj9K5VOd7k8oi6+ejNNFNTQoJWmu7F/X3iGGo0gcXbakdW5qtyesUESgrzfQaV1SfjsuiZ4VnTbwt+sRzSlVnv7IalB5HxROOFWrsNtWLlmOujBXcI3UspY1kTWVTs+GIsap6FvPTqk09uH/RMcYYY4wxxnQOv+gYY4wxxhhjOodfdIwxxhhjjDGdwy86xhhjjDHGmM4xvmQEUyhV+5drtiH2Ucr/28qiE1er29ef0l3ai5C+hZIRlDJV4A7aPiX6qPpdZC1QWQyo7MpyKZNU4tk1KlOGdyywVSIyJdYGi7PbEv6rMlWHkw80TUYQqaMYptBfjdoOJRZowsb2LM5fenWlbGuNBK5rQj68StvqPuE6ql7k/mpq/jpV9nuX7oFdIcLdUsJcKntuRdQ5ydvlDb98sjwpK7PVshO4WNRZohO1gueLOvMiDnHSgohQNiq85xinkhFw/OTYqeqotttMRsDJB66LfrNYuk2BL18DZRA7qRxBKY/nbfWcZyLSfFlWPmbLuC8upbq+ISPIpqL+NZ6QRGZNyjJVTeS4TEyQOH4FYh6A8rNMZVGpihbZlxeKx3wkSYc2I60fq6ptTvii69QngWlqEqwSNMzQeboeakcNgmb4Fx1jjDHGGGNM5/CLjjHGGGOMMaZz+EXHGGOMMcYY0znGptHZmUp44UT18Meu0tpN0tUAAF5AfR21VJiWPZ8Ryx+PPVvdviTaiSypPy7aPsFLWc+JHVXZWdq+XdShpasXe6WQZ7VYzFquKddrzI/Sdrl+ns0TAQBrqbrdph4mYgbKZUpnEdH2qDpNjU4joy1i3BbZL0KHdD15vYet/4fWh7NERN0DXKeUlegybqupjiek0RFlvMZcaW0i+pvTog6XnS4X518+U+545Uw1flw7VcaKk3Qy1Tp0Nrnsl1VjjNLo8Ppx3XYZqzimqTjIZTKe7oj4STqxzXVhrhwwjZ2eKz/vtRXSFvXK863W/kcI6To6zBGUahNWjCilCWttlKmoktkWc4aAMXgT00tAa8BCGh2OHQBwkc8Kq5GB2MNJaXvupGZSWYXDkOqjioP8eYW5MqPiidLFyfNLcByKjlMeh0prw/1UY1fFT9b8KaPomYAOT2lreB6pzhGfgx7F5YR6HdV++BcdY4wxxhhjTOfwi44xxhhjjDGmc/hFxxhjjDHGGNM5/KJjjDHGGGOM6RxjS0awjV4hmu/dXs0GsLCxW+4YMWtU8CcVYr9jy7QdTXQQERKeom2VVOAuUfaq+jovnKtKHp8tDgZcEiq9i1SmBLZsjKfM9NbXShFso0QDkaQCqixi8hgRi6uypkanikiiAS5TgtPIfpGR3aFkBNgAcJ7KOInA02I/Lrsg6qhkBLyfqlNQCkebZyOYr6+ihLks3j0j6nDZnaKOGBe729XAd0ns1jtV/bxRkTsLbNncLkrEFFklI+DYuLohkhGslvvtrtLDIJLcRFzLrbkyEDxHSWB2TgqzyNnq+VXGo0qYzGVKPNzlBAXTPeA2mg9ce47qiP1Yvq2GZfl0FsmKlkUlnlcIP0UlaufrpIwgi0d/JJEJAMzRWVi/U1RSFqmMMgylk6KabpqMgJItHJmqv5eVOWjMQLPcj5MIaMPQ8u7h8av6xCgjX048oMpWxKRpdqPeqHljVhmG1tvr8nniWO1kBMYYY4wxxhizB7/oGGOMMcYYYzqHX3SMMcYYY4wxncMvOsYYY4wxxpjOMbZkBFuYwVOsyCdd1am7qskJAGChRwkK1CdQ+iwW8intG6sElVhdac9YR6YE5Hw8lYyAEw8AwN3VzS2RjODJ3tnK9jNC7vgMbivKLpHYTyUjeJ7K1q4KUdma+MCRhAFN6qgylWiAy5RYPNK21Io3FMVtC2fnOtS9FCkb28geE5soEwlEEg1wmaqjkhjgBdp+RtQh9XKxD1DKlxVKzEtO4tvl+MbTItPAKomH1RgIiOMj9+DuYpmVZXWuGk9ml0txa1ORO++nnMyVeJcTrKiEK5ywYGO9FNzuRpKyND3fKg5NVT/LNXH8o7NV0TN/jn5Z+XlZLK0E1dc7nIwA00CiIXWGPu785XI3TpGhEhYcV8mKePhycgKgTFAgxqASsDMq+QQWt6rbJ0XPWfgPlIlLnjhW1tkWZYzqdiQpCpep5CqBBAUzcyrLVBUdT8o4wPVUPOMxtxRIKgCocVken1HH53aAMvnALc+VmVMSJ+gScWlhodyP4/zObHkuOfkAz0edjMAYY4wxxhhj9uAXHWOMMcYYY0zn8IuOMcYYY4wxpnOM0TB0qjC25PWlm7PlusXbX/VUZfvY3FZRR5ptcZlys+Ml9cowVK6Vpm21BpeP/wpR51xZxJqcJ5bLRahP4uyB24A2EWXdzkWxMJjN89aEKV7IjFOtTW9qGBoxA2VNTtQwFHw/NdVVKHjds9Je0PldF7oedQ9yWdRotCvsoF6XFTEMlXocFSyeoO1ST1jqdtS9FDEMVfcJi/7UPSnaXn91dfuiuL9YqqfWuDcc85vr1Zi+sVyuMVfrzrlMrTtnU0ulV1Btc72IzmFnezIHk9IZKP0Naw+UFmGGjAgjeoGJYRqFjnaaLvkp8ZzP9ExL6jZR8xPW6CgNMe8Xk3UU40LpMxZPVgf02ulby4buFI1zHFCfN1JHmXqyJkgdn6dDSkek4tdi9UJNhQxD6/U4UVgnpWKOMuldoqAaMQxVsVIZLi9drp6TpB5pHNPVaRNdWtiuautXbi8fIDzX5PhijY4xxhhjjDHG7MEvOsYYY4wxxpjO4RcdY4wxxhhjTOfwi44xxhhjjDGmc4xNUbkpDEPZzEyJG6/1qnVOnS2N+k4c56wCwByL/ZS+mLXCTQ1DF0Ud9swqcwPghdtLk66netVz9IRQ5H2Zygoj1n3KLpFKj7cB4NIlKrsoVO4Rw85xG4bKxANKHM43xhVRhxMWRDJUAGUyApHYoWhbGbAJM7fSoyvWpa6wi/r7KZLYQgoey3hS3heRgBK5lxSqDl9MZcqnEhRQ2bYwuWQi+RIaEhH+t0kkicEsSvNAfhbNL5Zi3ivbpTB5lw06pwLJRaRBa3lfHqE+qD5N0edVZpERg1Z1ncokDh0yEBXJCCJJhxLfOupaqqHKxxL+v8XxxPGVOH6Wrrk0i1ygZARnRDICZcbJMVUlweE6TZMRqOPfGahzsoyfiyvVWDwzJ0xUCZncZKdU3u+IOMDMzNYfT8Wh6/TAUteby1jU32+7PP40P67UI41NctWzQc1/iVvmygnL6vHqNeE+OhmBMcYYY4wxxuzBLzrGGGOMMcaYzuEXHWOMMcYYY0znGKNGZ7owtmTDoOvCKI/rrIrFnScW2CkQWLmrurbxlnOlsOPoWnUt57QyDA1odLJYp3pluaqruNgrzTmVRoa1NU8Jp9EnyWlUGYYqjQ4bhq5ulOdyiw1CI3oYoJn+pqlhaGi/cl2yXoTKLllNDUOFjqYYbhGTR7UIVrjJsW5HaXb48F3W7BxKoiec6ynDUF7oHzCfBQDWjKj11FwWqaPKRBzskTGfWoeudTQ7tF2OC9aj8DYAbDc0+Cv6M1u2PXOqXPe+SVqAjfVmppqzQkPA6/yV9mKJdGGqjroG6twxrAVo69weCpRGh+9nNT+IaCWVoThrdsvpQWEYuhU2DK1ey0WhFVyhh+qF00IXeKd4pvEwVHGhqUaHp0NKf1MYhgo9zkkx11uoPntVPGGUHmdTjOeN9foLU8QBYSKrxiobhEZMetVYnt86JlXBAAAgAElEQVQR8yHW3/A2UMpV1Wkrw0kxR05i7Mwfr14T1hMmVE1HbwT/omOMMcYYY4zpHKEXnZTSfSmlx1JK51NK7z2g3nenlHJK6d72umiM6RKOJ8aYNnAsMcbUUfuik1LqAfgAgLcAuAfAO1JK94h6SwB+EMBn2u6kMaYbOJ4YY9rAscQYEyHyi84bAJzPOT+ec94E8FEA94t6fwfATyK2StUYc3PieGKMaQPHEmNMLRF17B0AntyzfQHAG/dWSCm9HsDZnPOvp5R+dL+GUkoPAHgAABbOHReGoVVBLSceAIDnSbV2i1DCXxJKPhbbLfVKQd7R5apAa3Y5Zq7GoswNIRC7TiJglUThokhG8CwlDHhGOI1GEhZwOwBwaad6vMtPCwXkRTK4iyQeUGWROuox1LTtwlxKmT5GypomI1AowXgT1LClBAXbDY0Jh89Q4gmWzpVi4bptoBTPronzppI/FPeAMvVk8W70vomYgXIcuEPUEerdiMCXjfp4W7WjylbKc7JE4vyjQnCrRLgsTFWGd5GEBcr0L2KouEFBRomAVdzfmSVTzdl6gz+Feu6wEaA6J3ze1GeLnW+lMB47rcWSQd2X4sm5WwDK8VMmEVDJCCKnSWnV+dGrQg4dfycYv/nazYs4xPOj42c4KQ/w3KqKMYRKRsDPddVvtR/HExWHTlcbX1aJB2ZVAo6IQWh1XKo9toU56BYnGhB1dii5yKZINrIxW8YTjjuRBCAyucu2SDbC10nd31ym5mwqQUFg7JTxrLp9ZMiGoerJ/9IRU0pHAPwUgB+payjn/GDO+d6c872zt6qMQMaYjjOUeIKjwsnbGNNlWoslQDWe3BpwdzfGTAaRF50LQCVf8RkAT+3ZXgLwOgCfSik9AeDbADxk0Z8xRuB4YoxpA8cSY0wtkRedzwK4O6V0V0ppBsDbATz04h9zzpdzzidzznfmnO8E8AiAt+WcPzeUHhtjJhnHE2NMGziWGGNqqX3RyTlvA3gPgE8A+BKAj+Wcv5hSen9K6W3D7qAxpjs4nhhj2sCxxBgTISRnyzk/DOBhKnvfPnXfFGlzC9OFaP4KKdKWhMqcRfyXAi6//bbqHaJZGBsRvAKlaE2JS69RMgKVaEElKODECioZwTOUaOBZUUclOniOkw9cFM7HfCojyQFUmarDQrZIHVUmc+nwfaGSCpT3TlkvkoxAqe8iQ0u1HRGwR8rmRR2i3gx6KAwjnqCHUtDK20pAH7mXVpVNOV8npTnk+0slLFBw0opAMoIpMXZVooE7a7ZV2asDdQDgTPXzKUHzid7FyrZKJqOc21lArcTxLKBXsbopkRgfSSqgYEGxcjJXlMkXhOg4kKBBHS/S9mFgKLEEAGYAyvFTJhFoMxnBcs02IJIRlN9Tq+t0lMaOmh+dQHVcnuo9U9S5dmeZTGedsyaUU5jyuR5NRkBtHTlZnvCVk/VJptRcj1Gi/k15oQJw8oHt8gPv0nNmgxMYoExkAsSSD0Tobe+WhXzvqnuZn4+qjrq+XE88Z6d2KOb0+F4ebjICY4wxxhhjjJko/KJjjDHGGGOM6Rx+0THGGGOMMcZ0jvFYBgLYwlShJVmjde6rQosQ0dpwHVXG61aBct03r/kG9BpnRpnSsRkqG4gCWqPDZUprwzqei8IwdfWSWDy7Sg6KygyUy6KGoRHtQ5M6+5UV8PVtqnWJ7heB91PDr61+C82GLOsIPZTrw/k+UUM3YqJ6UZStkm5mTeloWkKte4+Y6SmNDpfdKepwmWhn+kypLzt1orquX2kBCuNmqcdR+sl6jQ7rE9iADojpXyJal+h+ESLPFIV6zpR16tf0t6U3aks/cCiYAgqP7SYanah8kw0VeVscf2eqPN/qXuLxpMbcSVyqbK+i1NdtLJealWdfXb3n1y6KYLUeeO7MlfrFOTIXXlouJxqR+aCKA4zS4/D4KjUj+zBF9YRGJ0JTzV+E3pj0uePCv+gYY4wxxhhjOodfdIwxxhhjjDGdwy86xhhjjDHGmM7hFx1jjDHGGGNM5xhbMoJtTBfmlywwZQNPAJgnZW7E+BMoRWvKDLSpYSijRGQbqBpCqWQEykSUy1TCAk4+sHq5rLN1UYilm5iBNk0YEKkTEYvLMmUkxeJG1VDUwLGOqLKPh1ukT6qPqozbUnXGNtyHzxS0ISjXYdioLiL8B2LjInJbzImySJ84+UDTZAQq0cDpaqIBTjIAACdIvAw0SzSgYrWKuywojsTmaDKC0mi0vHB8PNV2pE/DNDHdRGk6uEEi60gdoEyeo8XaHUo+wEwBRU6fSDICLos+GjgOqLhA8au3Xd5Ls73yHuQxt4Lnizo8ntVcRN07nDTh6EKZKOf61dJolJmZE2O+Vx1jOp7cuJEw0CxJh/r816fKz3aEkhHsTombgOpMcQIDNE+mEmFHPAunuUydosgUQvmscluine1enSlzChxc4190jDHGGGOMMZ3DLzrGGGOMMcaYzuEXHWOMMcYYY0znGJ9GZ2saX/tK1ZFrmtZpzi8K86dZqiN1PGXZakB/05ZGR8HrDa8Jjc51lOs9ea2sWju7drVatr5a1mls6snLS5tqdCJam6hGpxHqVm/LQDPaNpe1OfwimqSIqeiEMo1Sp8Lr3Fn7ApQ6tYgeB4jdu0xEIwSUmpzGGp1yYB4/XV2Lf6JXuqGyeaDS40RMmSM6lqgpM69Nj5h6RrQ2qp+87r9f1lRbtEl16o1OFVr3WV0Mr54f/JyJ6ED7faqeu+uij9x2pzQ7UwCOUxlrD9R45pgTMrcWbalTSbfzzPpuUeXobHlfsnZOGfmeIFdk1mgBpc64383qfaE0xNcWmt0nkXEZM3mvjxWqTxF9ndIWbW+T1kTob3qk21Hapsj8U2l2QvFkSvzGMUv3kzKtjRjiqv34OSfqsEErGxBna3SMMcYYY4wx5uv4RccYY4wxxhjTOfyiY4wxxhhjjOkcftExxhhjjDHGdI7xOQhuJeDpqnJva666fXlOiOrnqmqoOZGwYFYIxFik11TY1paYlIVWgDYRZcHntZ2yzsY6qSTXhchcicaaGna2RdO2Q/vxOVDCe3X7swizLVNRdbxIwgJFU6PRSDsTikpGEDHebGJsG4Uvt0o8EElGcLqdpAJAmViARcgAcAuJlRcDRn1AGRsjIuAoHD8j7USTEXBiAfXZONGCSnijEjTwM0TtFxFGR54XKsENJxqInv/SLLF0ARymGeq42e0BV5er3wMvoBT/F7BhqNLdl1OIGHR6p0U76h5cozIVF/heUnMRdQ8wUrBPHzjSjmorYgaqY04ZB1j4HolVKrnKfE9cYIrpO9si0QElKIgm1eIyNUdlZCKT2TKxxMICPWeWZWP1qMvLfvUiGQEnwOBrpGzho/gXHWOMMcYYY0zn8IuOMcYYY4wxpnP4RccYY4wxxhjTOfyiY4wxxhhjjOkc40tGsA0UOljuzZxwQqWEBeuLbEUMrM+VQuxriyS2E0kMZmbrhaNKkBZx7WYiTtf9sqpoTAnbVNnYaeIcH4Xvk23lmMuVIokHVJkS9XPCgEidaJ8iCQsiw7ZDiQYiTGXgJIkpV2hcRJJ0ND1tZRgCKA7NrZRC4aXltaJsBc9XtpsmFYjsx8cCgCVU+xQRAQNa9MuwwHRTuK0r4T3vF3FXjwqTOflARASsRN8qaQPXm90Q5227XuS+OVfeu7O9eiF2mVSgPN8sAgbK51xPiNO7zG5KhWC7R2LtuUjyHlVHTQ+4nkpYwGH/clllabZMXLK5XB3zap7BiSx43rHffpFxyFwXJ0CN+QhNjh/dj2MFz/OAfZIBcNPiUDxWVTw5KpKicJmaj3Lb6tzKhBDLdO9wYg1AzL1EHZFoACeo6eXyNxZOgMGxKr+M32X8i44xxhhjjDGmc/hFxxhjjDHGGNM5/KJjjDHGGGOM6RyHS6PD69xV77iOMvMT65m3aH3+5fVyDSqbj+4slmsbZ3rlfmxCF1kbzmvO+2XN1puy+dTulLBWmlJ6J64jGm96h0TkKEOF74GIHgcona0UrMmJCjv4eBHD0KYnTumG2jQ/PVz0prexTCaarF1rqmXj8QXE9HxctkJGnPuVsY7mpNDfNNXo8PH02vBqv5XJZkQPouJZRAsQMU6OaHvUmvpIbFbr7rlMm0mXZVM7ZDq4XupxxO0lKMfu9gIbXJfnpEsmnqMk40ihY5idous7FTAQVajHRcRENKD/UU+UEyTm2Vgu7xMeT2p8aRPRaj2l4+HzqOc59ZogNWeK6GgUEZPeun2A/Qzkb1zbEzUg5noq5jDqvCld3tXl6vEWNsT9rbSojNDorB+vbq/Olu7dHOObzocV/kXHGGOMMcYY0zn8omOMMcYYY4zpHH7RMcYYY4wxxnQOv+gYY4wxxhhjOsd4kxGwDrcwDBX7RZIRLO5zvJrGVVORtqd6N24YGjHXA0qx2c6sSGJAIuutuVLYx0ar/bKabSB2TZomMWhaJ3TXciVleFcacsUSCzRNRhAxA1UJEiLcZAahRA87WOpd4cLq5my9ED1qChcxkOSySOKBflk1sYBKKnAbnqmto9rmPi1tlP1mwXxMLA9skA752kIZLFjQfAVLRZ0rgeQHSkwrTfAaoESwkUQLqmy7R/tNNUsIsjNVfidZmq/WGzrqfqvEOPUGrZG2bzp4rKgkA6qMfYMjY04ZOoq2+SlzauprRZ2dheq1Y2F4tEyP5+qkSZnPapPgyP0l5jqETkBy40k6ou1wjyImxZHnR3+/6nMnYhiqzptKLHFltnrtesdLR9peIFfT5lwZq7jtVahkBNWYzskodm0YaowxxhhjjDFfxy86xhhjjDHGmM7hFx1jjDHGGGNM5xifRmcH7Wh0lB5HwW3zmlgAmCKDqrlyrbg0HQwsTea1/3qNqDgeNS7XiS7U6zPUx8U2nUwlUuIyadAqyoZpGBpqi9fwKz2O0sPwuVQHU201QWl0IjqeCE20RpPLEewUa5p5rKi10qyBi+hxAKF1EeupWSOjNDrKDPQ2PFvZPkV6HNW2Mgy95blysCaW7ah1/pFbRwyLuQXeLo9/9QSdXyGraa6RYc1K/fr9fr1qJ5ROS+kTIrD2YGeh3vRQofQ3vKZd6Za43+pzqPNUGkGWdfgaKJ1Fp1GXjcvU81I9jHkcqnHJqEeT0v9Qn9Tj+uSrqoHhSk9pbcoy1lqoWBkxtdT3942bBCsiuks1BiNzNqWR4f2OivlCE42nakubObM5fXmOlLErX1/WbQHledNtl9dpjdpW9xLrhmwYaowxxhhjjDEHEHrRSSndl1J6LKV0PqX03n3q/MWU0qMppS+mlD7cbjeNMV3B8cQY0waOJcaYOmoXAaWUegA+AOA/AXABwGdTSg/lnB/dU+duAH8TwJ/JOT+fUrptWB02xkwujifGmDZwLDHGRIj8ovMGAOdzzo/nnDcBfBTA/VTn3QA+kHN+HgByzs/CGGNKHE+MMW3gWGKMqSUi674DwJN7ti8AeCPVeQ0ApJQ+jb40/ydyzv+SG0opPQDgAQDAsXOlKI97ExH7KdSnYlGgUuRtp9qme8I9j0Vqs0KgxiIuVUfBYjslli6EdAtFFckaJ1ZYF8J3Pm8yiUOgrGmd1lCi/ohh56jzdTRNPhBJLHAoTEWHEk9mzt1Wm4xACUfZuE2JaY8GkhHoRAOcMKA08GycjODqc5XtOTV9Kw8HvEDbSiwduU1U/OS4c0JUmaqakW7cVp5bJWZlYXITwz9Ai5eLhAEN66gy7vd1cS9FDP4ibatEAywWVyLgiBFkxIx0DLQWSwZ1XoonZ86lMn6Qka58hEeSCvAYBAD2ZowkCVGPJrVfwAn92Fz1+XHybBk8VnFLUcZxUM1PmKiAne/V61fLZBvbKjkUMSXmbDOUaGqmV15MFvqrZ4MiMtfjttQzRh0vYhjKqPOtEjtwMpPI+I4mOuDjqZjD9wC3nVE/P9+PyCxOtZ5FO3cDeBOAMwD+VUrpdTnnytM/5/wggAcBIL3iXm7DGNN9hhJPFu99reOJMTcXrcUSoBpPvvnenuOJMR0hsnTtAoCze7bPAHhK1Pm1nPNWzvnLAB5DP7gYY8xeHE+MMW3gWGKMqSXyovNZAHenlO5KKc0AeDuAh6jOrwL4DgBIKZ1E/+fix9vsqDGmEzieGGPawLHEGFNL7YtOznkbwHsAfALAlwB8LOf8xZTS+1NKbxtU+wSASymlRwF8EsBfzzmrFeLGmJsYxxNjTBs4lhhjIoSU1jnnhwE8TGXv2/P/DOCHB/9i7KBZMgImknhAlTXUZfd69ckIIu7qoaQCAiUKZbGbcgJWCQpYyLe+frysxOdtUXRKCZNHreEv4OXbqkNK+B8R9TOjFvlHjnd4kxMMI54cwW4h6GQxadNkBMqhmpMPqGQEnHxAJR5QCQpOUL2VjbLtueeoIJJ4AChFzypWcvhQmlSVC4CHmGqbLsHshhAvlyGukWA/IoqNwm2xizeghf58P3FyACCWWCEiKFYCX+6T6qMqqxMGAypBw+iD/lDmJv09i+fxNIcPJfznMh5v+5Xx+FVtR/IXqUREgWQEvN+J4xxggGcWyjjIMVfFWL6/1RiU4+lydbKxvlrep0UCJfVImxNyq7lqP+cWRdxfrk5Q1TiNJF9QdbiMn0MAcFSU8flVcz05/yNUbGTUXJNpmoxAJZ+IHK8pIcNQY4wxxhhjjJkk/KJjjDHGGGOM6Rx+0THGGGOMMcZ0jvEpKXZRanSU1oOJmEw2NRolelPlTkpHw+uwI+v81brNiIloZP1j1ExvZ7l68jbXy7Z312jxrrpGkbKxa3YUEd1OU6HYMPUvTfU3vN+hMBBthSPYLdY581hV66Ajxm2LQqPD9SLmbmrNtdqv0BaxUSFQrrtXQ77p5eVl1+r2VrqdiH8k1dmZiplO8lrwiEZHaQHU2nDWlqi2Oaaq5wBfN6DUv6j9ImvqldEpr2nXJoD1pqJsFAiUmomIeWBEYzopHMm51I/x41lpX3hOo3RySk/Hkhil44lodJTMgfdT43m5ujl3qqyyJDQ6fM+rMcD3jrqXr+2U9+X6RTIovShsk5SBOTMl9lusTlDWV+r1Ib3l+rmf3E+MC9baqLmfLque38hcT8cOFQer9VTbTeIwUM5bo/u1hX/RMcYYY4wxxnQOv+gYY4wxxhhjOodfdIwxxhhjjDGdwy86xhhjjDHGmM5xuJIRNNF9KyF8U03kVNVYamqqFGMpsR0Ly5SguRQv15tBKZSg+boQkzIRgdj1xbKdtUVKRtDUMFTdaU3vvtD1ZZOwljJUtEoTc1KgeaKBpsebBEqDv4hhKCcFUeNbicXLtpUBcCwpSB07YpxM85hTRoGR2zvi0abGqdqP+7BcVtmiOtd6pQhZGcdxEgElsOV4phIPKGPCJsaXSmBcSrXLe0fdE00NQ5sYpEZNVCMC8i6TdkUSEE4+0NQwNJKgoPTrjCUgicQBNXZP0Lbo99LZ8g5nMX7kXlbj+9qamMOsUhKB0m8ZhU+zNAwVZTyP2S4TFqyjmgzh+lwZ46/NlvMxZTDNcFyImIoCKilKs2dMW8kAVDxpq23+bKmY08XxLzrGGGOMMcaYzuEXHWOMMcYYY0zn8IuOMcYYY4wxpnOMV6OjDLf2EtF1RKUXjQxDy/WPap1/xDSLNTlqHWdEo6PWj7OxlFq3GVnTfmVhqaiztkgXaU4seI1odCK0ei25ktKnRMtu9FjRdiIfRLXT1n7d0ewcQa7VzSitDWstwma7AeM01j5EDB0BsTZbrLtfoUX0cxHNDBAzPWQaanTWhUbnykJ1cTwbau5XxtoapbUpz7fS+tRrVNrUo0TMQJsS0RY1Nfhrcg6G+VlHTcrANI8V1t8ojQ7rb1SdiGGoqqPaalJHxQU+ntDozO+U2uOZXjnXqUNp59bXyvFc6G+aanRU/FrRfavuV9XtXFks49LSqXIeV4659qbZTZ5XbR4/Yq7cNJ5E9IxN8S86xhhjjDHGmM7hFx1jjDHGGGNM5/CLjjHGGGOMMaZz+EXHGGOMMcYY0zkmLxkBC99VGxGtdqCOSkagBc1s/qSMCTdq6ygTUUYJvVigpoRf80K8u0Z9YPMvAJgmk6ytKZF5IHIXqTpNr1MoGUFTA02+Bl1KNNCd5ANMQhZmak2EmzEhJYvaVVKBiLmbNvKtinWvCUNgThwyvyCEwiIpyuwOJS7Zbib43JgtBcUs/lf95iQC0WQEXKbON5cp0XPk+jYV9Y+bpn1S93cksUBb5oWHkowypPK2yh3E8xFlGKoSBnC9yH5Rw1C+vCJJSHE80ceja+Xzo7d8cMwFguL8dZHdhA3lOfGAKlPzwaZzD5rq7K6UMWfjVH3Ck6bifHUuI7RmVN0wDjZJPACoeFL9/DYMNcYYY4wxxpg9+EXHGGOMMcYY0zn8omOMMcYYY4zpHH7RMcYYY4wxxnSO8SYjYLEZo3rHYjOl12qaoKAhLKJSQit2bedt1U6/rNrxiMvtphIhywQJ1Xry+FPV429F7xiu12bigaJMidQ4qUBU+B8R9UfqKJokGogmQ2grQcLkUifC1C7O7PRctsEi+yibgaQCKgHJ82TbrerweFaJB1TCk6keCT57zYSrERGqOm/lORECX5FEgJMYqP24bXV8naDgxt2+2xT+jxL1bIoJg29cPDzR7KJMNsDbap7BIv5IHVUm6my9IPYjptUl4GGg5l382cTxRW6mIg5FkljIsbOeRBltq35HkhGI/EmhOnw80cfrV8uYvrlQjTGjTlzCx4smJ4jMLSM0SWSiyniO7GQExhhjjDHGGLMHv+gYY4wxxhhjOodfdIwxxhhjjDGd43AZhnJvIhqdqB4nou3Zrq7B3NkWayuHuNxSrXGOrXeMmE+NeI11E01OY3NQZbTKO6o6kbJhmnpG92vSdnfNQZui9RGlZiOyH+tBWEPSb5k1cOX1j2j1IvtF9H1A8/XTESJr0SPmnHxuo/vx8SN6HLVfmxw2TU7kOaDKIvdSpwxDFXwK1MdtYiqqysR+1+sM1vdhuk5rpMrEoyqJ47d2zSPP/qZ1lLaHw0Dkmog622KOyGNexYCYLnC00/MmevCm1z/yvOI61ugYY4wxxhhjzB78omOMMcYYY4zpHH7RMcYYY4wxxnQOv+gYY4wxxhhjOsf4khFkDC8ZQSRBQaDOxnopZt2YrTfB0+KzesO5iPgrIqaNGN7p/UTb21TW1NSzrToSVekabStxvkpG0MSwM2rq2aRONEFEpE83F03G0zDF45FEAG3SplC0rDO8RAeTIGo/jH1sem6lsezNbhgK1IdQ9Xc+TZE6oiyL/bYCp3dK1ClMRJs+iwNEk10UNJ2JRvodabuhoXkxP4JKNDC8BCjDTKQyzPHcLMGOkxEYY4wxxhhjzEv4RccYY4wxxhjTOfyiY4wxxhhjjOkc3TQMVWVsGhWos7ku9DjLQrcTMA+8RnqQ6BpvrqfWZG5g9sDt/vHLPrG2SBn1bbFOKXq+m1ynaNvFWs2IgWZEj6PqjVt/EzX+bLOtbhDRrpX7HA21PUoDy4i+rk1tUVOTSdZ6KDPUWB1lolq9vyNtK81QUw3BMPVOkfuyrT62aVrbecPQNmZHqg11uQPDd5raUpodriP7EKkzROR90rTfc7Qd1ehE2g600xOiqCbalriuenxTdqA93U4kNvOx0ss4nn/RMcYYY4wxxnSO0ItOSum+lNJjKaXzKaX3ir+fSyl9MqX0+ZTSF1JKb22/q8aYLuB4YoxpA8cSY0wdtS86KaUegA8AeAuAewC8I6V0D1X7WwA+lnN+PYC3A/iZtjtqjJl8HE+MMW3gWGKMiRD5RecNAM7nnB/POW8C+CiA+6lOBnBs8P9lAE+110VjTIdwPDHGtIFjiTGmloiy6Q4AT+7ZvgDgjVTnJwD8RkrprwFYAPBm1VBK6QEADwAAjpzbR2heA4vPOMnAfmV8rMB+u2ulgP/KylJRdnS2ak45L4TvSmDLKPFZk2QEV7BY1LmCst9cdmWjrIM1OuGRc6vKIiauYcPQJokGmjqdKgF/U3PONg1Cm7R9KBhKPJk/d6JWxB8ZO7pOmURgk/ZTiTy4bVVHJQnh423uiKQolCRkQyRO2dlulqCgN0WiUCG4nZ0TyQBmq2XzhWkvsIQrtXWOivEc269aFklYoOo1T1hQP3aVmLittpsatuqy6vFi4uGRJyNoLZYA1Xhy7g7UC9YjiQaiwn8qSzzPAXCUTq80By3DQKxPkToBIvfXrBiDmBPPr7np6nY5rYnNISNJDFTbXCauydRU+XnLJB0qYcHhS9wxSkPrUX/+yC86KtkBp716B4AP5pzPAHgrgA+llIq2c84P5pzvzTnfiyO33nhvjTGTzlDiydyt6klljOkwrcUSoBpPbj3eck+NMWMj8qJzAcDZPdtnUP78+y4AHwOAnPPvov/ue7KNDhpjOoXjiTGmDRxLjDG1RF50Pgvg7pTSXSmlGfQFfQ9RnT8G8J0AkFL6E+gHk6+12VFjTCdwPDHGtIFjiTGmltoXnZzzNoD3APgEgC+hn8Hkiyml96eU3jao9iMA3p1S+gMAHwHwfTln/gnZGHOT43hijGkDxxJjTISQ5Czn/DCAh6nsfXv+/yiAP3NDR86oF5Kp3rEYXgjEpGB+lbYj4rOL5RLgK4ulYH/mVFXMGhF1KdHzNeHKzm0pB3QWRqvEA6tYKcqep7IrqyIZAZ+3yLlV9SJJI8LJKVisrIT4TZIKqLKmbSuaJBpommSgHQfjYTCMeJKRijHFon4lBOcEASo5QCSJgNqPx/Pa1XJ8XRMJT3ZXF6oFTcdOOLkHHZ8d2EWMXVdxl+PnSnnvLp6sBovFhStFnVtEQLlO53JF1CmTT6gkJSWleLhZcoBI3G9T8BvrZ70wOpJooOnxh81Q5iZAX/1TJ9BXwn8eF6qOKqMhr/T607ytxjK3A5TjUtWJ9GyK1eUAABAzSURBVLthggJOAKISMx0RyU12V+gTl1OYMp6puKQoYlWgzmIZz44u1CeeajNJSFs07VNbjPrzhgxDjTHGGGOMMWaS8IuOMcYYY4wxpnP4RccYY4wxxhjTORquumyBHZTrzCOygohpV1ODKK4j1nvuzpULXC9xwalyP9bWKD2OMriLGYZWNQTXhV6A9TgAcOlSNcvm7tNi8e7Fmm2guUYnYipa2CIAMcNOXjsbFSxE2h6mYWiEw6u/GRe7SLWaHDXmWN+m6qjxFDHbZc1bob0BYuOprfEFNIuxET0OUK5zP8mqAmBtteqftnayPG+bp0uBwEZPiQZunIh5X5vrx0e9zr2sU2/8aQQJ9ZqciNZG6WGWRdlV2laXKeL/q8bqsUCfuEy0sxUYghENmDIMXVoptXqXV6hTSkfDqJin5oMRjQ4lIZ9eLPU4yriYP5/SJHGdaJyIGAc3ZZQGwIfRMNQYY4wxxhhjJgq/6BhjjDHGGGM6h190jDHGGGOMMZ3DLzrGGGOMMcaYzjG+ZARAKYxVglqmrWQESrQXEeGKtndJyfe1bWHqebIqlL4yW4pwW0tGsFGKpy9fFGq7i/QBL5RV8HTNNqAF1VzW2DBUmf5xmaoTMf5sagba1DC0rp02aSvxwaSQiuQDbPQZMQzl5ATAPmagO9UyZba7e5HEtJFxosoiCQsOQzICEu+G+rRdNv4cTpT7na5u9nrNkgooYXBELM1xt01RcFvC3GEKlRXqWdQVcirF99OROUQkGYEqE7d8QSQfh6rDbatjcYIE0ccdMfeJ3AM85o6K5/XSrEpGQB09XSY3Ka5Bi8kIjpysZohYOVFmhVlC2e9FKlMJC/ic6AQohz/p0GEwDY7gX3SMMcYYY4wxncMvOsYYY4wxxpjO4RcdY4wxxhhjTOcYr0anMINM1c2IZidKRNsTQS2b5HWha+Xi3cur1UXmlxdLDcWROaHRmSKNjtD/7K5XdQZYE2tZlekgr/1X+hsui+oMIqaHRZkyB41odCI6mojxpyqL7ld3/Cg3m7amHTJSsV6cTXpZjwMAG7SonfVuwD4anbWqsejuWlmnuL+jYycy5iIaHVXWRKMTMQdt2rbUU5bx89pi9XzPL4trQnFB6XFmhP6G15mr/UqtZLMHyLjXtPOYMJp8BNicq34PPD27W60U0d8oc9DyFixRt1ehbxN11Fhlw1DeVmXis23OlfMKvp+UIS1r3pRmRWldlk9XrdgvK3ER90mdE6nRqc41poVhKWtyVvB8UUf1e57i0NGAqajSBapzyWURc+Oo1idmQDwZmhzGv+gYY4wxxhhjOodfdIwxxhhjjDGdwy86xhhjjDHGmM7hFx1jjDHGGGNM5xhjMoIdoBByHaVtIapvM0EBE9FshZIRiDos3hXCvl1Vxleo6fEjyQAiwuhIwgLVlmq7+CylsA94QZRFDEO5rBQE6pPZxDBUMcykAoffSGwccDICFowrczsuU4ahOzvCAHid6q2nok4xLpWZXSSJQKSOGt/qeFwWNVyOwPs1NQkWZeuU7GFjuWy8NH8tE0vI60uxQu3Hgt6oKFcJisvjsxlpM8Fv8wQJZTxpkrSg6fEPI7spYWO2eh8sLNCNqZIRcPKBq6JOk6Qd0f2UiSn3qaFh6Eav3rE0YtKrBPyqbGWWgtrpogquLVbjgkrWxAmdAGB2rir+X1woj38LOBlBGWRVGX8WTk4AlKapOnFKfVKUpqaik5B4gOOJSlUVxb/oGGOMMcYYYzqHX3SMMcYYY4wxncMvOsYYY4wxxpjO4RcdY4wxxhhjTOcYczICFpqzgFskI8BSdXNNiIAjGnMF11H7RAS2yjWcHYuVaDAiDI58tqhLOicIUILmSFKBSJkSHRcJAlTigUiCApWMgO8ldeKa7hdJNDDuhAHDTIZgCiLjMhqXmuzXVswDYjGnLRr2WyWI2O5xMor65BOqTAnxI4ktlHg3Iurn/aJtt0XTJAKqn10h40iRuGJrtvoQm1bJCLjseMMOKN1/5BZQ+3GigdsCdcRnU0k6+N7RyQiqwv+jIjGQEvXz2OnNlm0vzVbnB2q8qYQgTRIkyIQJgQQFar95OgezdI4AnWiAz6/6bONOInAY8S86xhhjjDHGmM7hFx1jjDHGGGNM5/CLjjHGGGOMMaZzjFGjs4t6rQUbiAKl9uBYWWVdaHtYMxIx3mxq8Md6HFUW1ehEaNpvXl7a1FQ0ZFaoDDufq9kGYmagTetEzECbGoYqxq2bGbduaLjUmamptcvlPqJOT5SRCV1h7AuU4zlqzsmxIRIrVB1FE9PBSDxTZapPkc8myo7Q+VbXhNerR9eqN9GaRPU/TQ39xk2X9TcRMhI2SJNyfbE6r5i+KuI5a12il5/Hc1Oj0YhGJ2AYuqUMQ4VGh1Fjjg0zlWblekCjo/QoG/IDc5/KEzdLGh2lGyo1OuUkKqLtYT2OKlPH5z4Cpd4pEuMipsVdx7/oGGOMMcYYYzqHX3SMMcYYY4wxncMvOsYYY4wxxpjO4RcdY4wxxhhjTOcYczICFohHhOARQ8elsmh7vrqtRPVtifqVULeJwFgRMdhT/VZl3O+mCQuQRRmL9C4F6ijD0LaSEUSTCkTur7p9ooxaqDzuZAjDIyHXmqmxSRxQiq6VKZ4qm52rikK3FsW5JfGyjAvKXDgSh/jWUbFDmvQGiCQjUP3mspOBOtJcuYwn84v1Bnt8fbV5YXkPxEz4bjyxhWorul/d8dtEGYZG+tTlhAUZqTAM3eiRgehCOean+bRFDHlVmTIjjYx5dUl4/CoTU0pGwIkXgJixrBb+V8cqJycAtKi/bKc+fjc1DFUJAzhBgDb+rP8sqm0+vk48UJbF4kk3EqC0aXzqX3SMMcYYY4wxncMvOsYYY4wxxpjO4RcdY4wxxhhjTOcYo0ZnB6VGg7sjjD8bG0FyPWE0ukbHU2vclbamiVFe1DwwQlONDmtylEanWO6pzq0y+mS9TUR/o9bpKqPROm0XENPaNDUMrdunTbqrq2mThCx1GzeKWuMtyxZobfh2WWd9nRbDR2+TyFp8jifRMd+k7TY1Oqe5TqnHWTxdCihXFqriwBUhFuS18HpNff1afLU2ntfQ6zoR3VB5EzQ1Om2C0tVEDAXVGJhULUCEXaRCm8fbSscytVON1ymq0WHfSxXKmpj9qrbZQBTAFk2HWI8EaK0io+6liNZFm+3euMYy0k6/reoJVhqZ0tSznGu2ZQY66njSVIPXNDa1Y8qcGh0bCPyik1L6uZTSsymlf7/P31NK6adTSudTSl9IKX1L494YYzqN44kxpi0cT4wxdUSWrn0QwH0H/P0tAO4e/HsAwP/28rtljOkoH4TjiTGmHT4IxxNjzAHUvujknH8Hem3Si9wP4Bdyn0cArKSUXtFWB40x3cHxxBjTFo4nxpg62khGcAeAJ/dsXxiUGWPMjeJ4YoxpC8cTY25y2khGoBRCykESKaUH0P/5GAA2gO+Q62oPDUr8t4aTYLtRJeI/fJT9ngzc79Hy2jEfv3E8+efpLx3ueKKZ1PtkaP1W4ZTLLjRv3ud7tExsPHldenzS4kmL90hkFALA420cbFLvbfd7tDSOJW286FwAcHbP9hkAT6mKOecHATwIACmlz+Wc723h+CPF/R4t7vdoSSl9bsxdcDyZANzv0TLJ/R5zF26aeDKJfQbc71Ezyf1uum8bS9ceAvC9g+wm3wbgcs75qy20a4y5+XA8Mca0heOJMTc5tb/opJQ+AuBNAE6mlC4A+HEMDG5yzj8L4GEAbwVwHn3Tk/9mWJ01xkw2jifGmLZwPDHG1FH7opNzfkfN3zOAv9rg2A822Ocw4H6PFvd7tAy1344nBe73aHG/R4vjyeiYxD4D7veouen6nfpxwBhjjDHGGGO6QxsaHWOMMcYYY4w5VAz9RSeldF9K6bGU0vmU0nvF32dTSr84+PtnUkp3DrtPEQL9/uGU0qMppS+klH4rpfTKcfSTqev3nnrfnVLKKaVDkX0j0u+U0l8cnPMvppQ+POo+KgL3ybmU0idTSp8f3CtvHUc/qU8/l1J6NqUk06cOhLs/PfhMX0gpfcuo+7gfkxhPHEtGi2PJaJnUeDKJsQRwPBk1jiejY2ixJOc8tH8AegD+PwCvAjAD4A8A3EN1/jsAPzv4/9sB/OIw+9Riv78DwPzg/z8wKf0e1FsC8DsAHgFw7yT0G8DdAD4P4JbB9m0T0u8HAfzA4P/3AHjiEPT7PwbwLQD+/T5/fyuAj6PvQfFtAD4z7j7fwPk+VPHEseTw9duxpPW+T1w8mcRYcgP9djwZ7fl2PGmv30OJJcP+RecNAM7nnB/POW8C+CiA+6nO/QB+fvD/XwbwnSklZfI1Smr7nXP+ZM752mDzEfTz84+byPkGgL8D4CcBrI+ycwcQ6fe7AXwg5/w8AOScnx1xHxWRfmcAxwb/X8Y+Hg6jJOf8OwCeO6DK/QB+Ifd5BMBKSukVo+ndgUxiPHEsGS2OJSNmQuPJJMYSwPFk1DiejJBhxZJhv+jcAeDJPdsXBmWyTs55G8BlACeG3K86Iv3ey7vQf8scN7X9Tim9HsDZnPOvj7JjNUTO92sAvCal9OmU0iMppftG1rv9ifT7JwC8M/VTnz4M4K+Npmsvixu9/0fFJMYTx5LR4lhy+DiM8WQSYwngeDJqHE8OF41iSW166ZeJ+vaD07xF6oyacJ9SSu8EcC+Abx9qj2Ic2O+U0hEAPwXg+0bVoSCR8z2F/k/Eb0L/G6p/lVJ6Xc55dch9O4hIv98B4IM553+YUvrTAD406Pfu8LvXmMM4JoHJjCeOJaPFseTwcdjGJDCZsQRwPBk1jieHi0Zjcti/6FwAcHbP9hmUP4+9VCelNIX+T2gH/XQ1CiL9RkrpzQB+DMDbcs4bI+rbQdT1ewnA6wB8KqX0BPprHB86BKK/6H3yaznnrZzzlwE8hn5wGSeRfr8LwMcAIOf8uwDmAJwcSe+aE7r/x8AkxhPHktHiWHL4OIzxZBJjCeB4MmocTw4XzWLJkIVFUwAeB3AXvi6I+iaq81dRFfx9bJh9arHfr0df7HX3uPt7I/2m+p/C4RD8Rc73fQB+fvD/k+j/fHliAvr9cQDfN/j/nxgMynQIzvmd2F/w9+dRFfz9m3H39wbO96GKJ44lh6/fjiVD6f9ExZNJjCU30G/Hk9Geb8eTdvveeiwZRaffCuAPBwPvxwZl70f/mwag/xb5SwDOA/g3AF417hMd7PdvAngGwO8P/j007j5H+k11D0UwCZ7vBOB/BvAogH8H4O3j7nOw3/cA+PQg0Pw+gD97CPr8EQBfBbCF/jck7wLw/QC+f8+5/sDgM/27w3KPBM/3oYsnjiWHq9+OJa33eyLjySTGkmC/HU9Ge74dT9rr81BiSRrsbIwxxhhjjDGdYeiGocYYY4wxxhgzavyiY4wxxhhjjOkcftExxhhjjDHGdA6/6BhjjDHGGGM6h190jDHGGGOMMZ3DLzrGGGOMMcaYzuEXHWOMMcYYY0zn8IuOMcYYY4wxpnP8/2kK2WRQfsdGAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1008x720 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set into eval mode\n",
+    "model.eval()\n",
+    "likelihood.eval()\n",
+    "\n",
+    "# Initialize plots\n",
+    "fig, ax = plt.subplots(2, 3, figsize=(14, 10))\n",
+    "\n",
+    "# Test points\n",
+    "n1, n2 = 50, 50\n",
+    "xv, yv = torch.meshgrid([torch.linspace(0, 1, n1), torch.linspace(0, 1, n2)])\n",
+    "f, dfx, dfy = franke(xv, yv)\n",
+    "\n",
+    "# Make predictions\n",
+    "with torch.no_grad(), gpytorch.settings.max_cg_iterations(50):\n",
+    "    test_x = torch.stack([xv.reshape(n1*n2, 1), yv.reshape(n1*n2, 1)], -1).squeeze(1)\n",
+    "    predictions = likelihood(model(test_x))\n",
+    "    mean = predictions.mean\n",
+    "\n",
+    "extent = (xv.min(), xv.max(), yv.max(), yv.min())\n",
+    "ax[0, 0].imshow(f, extent=extent, cmap=cm.jet)\n",
+    "ax[0, 0].set_title('True values')\n",
+    "ax[0, 1].imshow(dfx, extent=extent, cmap=cm.jet)\n",
+    "ax[0, 1].set_title('True x-derivatives')\n",
+    "ax[0, 2].imshow(dfy, extent=extent, cmap=cm.jet)\n",
+    "ax[0, 2].set_title('True y-derivatives')\n",
+    "\n",
+    "ax[1, 0].imshow(mean[:, 0].detach().numpy().reshape(n1, n2), extent=extent, cmap=cm.jet)\n",
+    "ax[1, 0].set_title('Predicted values')\n",
+    "ax[1, 1].imshow(mean[:, 1].detach().numpy().reshape(n1, n2), extent=extent, cmap=cm.jet)\n",
+    "ax[1, 1].set_title('Predicted x-derivatives')\n",
+    "ax[1, 2].imshow(mean[:, 2].detach().numpy().reshape(n1, n2), extent=extent, cmap=cm.jet)\n",
+    "ax[1, 2].set_title('Predicted y-derivatives')\n",
+    "\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/Simple_GP_Derivative_Example-2d.ipynb b/examples/Simple_GP_Derivative_Example-2d.ipynb
index a66b3f58a..2749027f3 100644
--- a/examples/Simple_GP_Derivative_Example-2d.ipynb
+++ b/examples/Simple_GP_Derivative_Example-2d.ipynb
@@ -26,7 +26,7 @@
    "source": [
     "def franke(X, Y):\n",
     "    term1 = .75*torch.exp(-((9*X - 2).pow(2) + (9*Y - 2).pow(2)) / 4)\n",
-    "    term2 = .75*torch.exp(-((9*X + 1).pow(2))/49 - (9*Y + 1) / 10)\n",
+    "    term2 = .75*torch.exp(-((9*X + 1).pow(2)) / 49 - (9*Y + 1) / 10)\n",
     "    term3 = .5*torch.exp(-((9*X - 7).pow(2) + (9*Y - 3).pow(2)) / 4)\n",
     "    term4 = .2*torch.exp(-(9*X - 4).pow(2) - (9*Y - 7).pow(2))\n",
     "    \n",
@@ -78,7 +78,7 @@
     "        return gpytorch.distributions.MultitaskMultivariateNormal(mean_x, covar_x)\n",
     "\n",
     "likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihood(num_tasks=3)\n",
-    "likelihood.initialize(noise=0.01*train_y.std())  # Require no less than 1% noise (approximately)\n",
+    "likelihood.initialize(noise=0.01*train_y.std())  # Require at least 1% noise (approximately)\n",
     "likelihood.raw_noise.requires_grad = False\n",
     "model = GPModelWithDerivatives(train_x, train_y, likelihood)"
    ]
@@ -92,56 +92,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iter 1/50 - Loss: 121.481   log_lengthscale: -0.367   log_noise: -4.714\n",
-      "Iter 2/50 - Loss: 117.704   log_lengthscale: -0.439   log_noise: -4.714\n",
-      "Iter 3/50 - Loss: 114.238   log_lengthscale: -0.514   log_noise: -4.714\n",
-      "Iter 4/50 - Loss: 109.993   log_lengthscale: -0.590   log_noise: -4.714\n",
-      "Iter 5/50 - Loss: 106.198   log_lengthscale: -0.668   log_noise: -4.714\n",
-      "Iter 6/50 - Loss: 101.540   log_lengthscale: -0.746   log_noise: -4.714\n",
-      "Iter 7/50 - Loss: 97.518   log_lengthscale: -0.826   log_noise: -4.714\n",
-      "Iter 8/50 - Loss: 92.896   log_lengthscale: -0.907   log_noise: -4.714\n",
-      "Iter 9/50 - Loss: 88.487   log_lengthscale: -0.989   log_noise: -4.714\n",
-      "Iter 10/50 - Loss: 82.971   log_lengthscale: -1.072   log_noise: -4.714\n",
-      "Iter 11/50 - Loss: 79.673   log_lengthscale: -1.154   log_noise: -4.714\n",
-      "Iter 12/50 - Loss: 77.952   log_lengthscale: -1.219   log_noise: -4.714\n",
-      "Iter 13/50 - Loss: 77.443   log_lengthscale: -1.264   log_noise: -4.714\n",
-      "Iter 14/50 - Loss: 71.981   log_lengthscale: -1.279   log_noise: -4.714\n",
-      "Iter 15/50 - Loss: 67.413   log_lengthscale: -1.275   log_noise: -4.714\n",
-      "Iter 16/50 - Loss: 62.697   log_lengthscale: -1.256   log_noise: -4.714\n",
-      "Iter 17/50 - Loss: 57.064   log_lengthscale: -1.230   log_noise: -4.714\n",
-      "Iter 18/50 - Loss: 53.132   log_lengthscale: -1.203   log_noise: -4.714\n",
-      "Iter 19/50 - Loss: 49.381   log_lengthscale: -1.179   log_noise: -4.714\n",
-      "Iter 20/50 - Loss: 44.819   log_lengthscale: -1.161   log_noise: -4.714\n",
-      "Iter 21/50 - Loss: 43.359   log_lengthscale: -1.157   log_noise: -4.714\n",
-      "Iter 22/50 - Loss: 38.582   log_lengthscale: -1.166   log_noise: -4.714\n",
-      "Iter 23/50 - Loss: 36.792   log_lengthscale: -1.188   log_noise: -4.714\n",
-      "Iter 24/50 - Loss: 29.682   log_lengthscale: -1.222   log_noise: -4.714\n",
-      "Iter 25/50 - Loss: 27.097   log_lengthscale: -1.262   log_noise: -4.714\n",
-      "Iter 26/50 - Loss: 22.476   log_lengthscale: -1.306   log_noise: -4.714\n",
-      "Iter 27/50 - Loss: 19.876   log_lengthscale: -1.349   log_noise: -4.714\n",
-      "Iter 28/50 - Loss: 17.554   log_lengthscale: -1.384   log_noise: -4.714\n",
-      "Iter 29/50 - Loss: 15.018   log_lengthscale: -1.405   log_noise: -4.714\n",
-      "Iter 30/50 - Loss: 9.520   log_lengthscale: -1.411   log_noise: -4.714\n",
-      "Iter 31/50 - Loss: 8.101   log_lengthscale: -1.406   log_noise: -4.714\n",
-      "Iter 32/50 - Loss: 2.132   log_lengthscale: -1.395   log_noise: -4.714\n",
-      "Iter 33/50 - Loss: 0.007   log_lengthscale: -1.384   log_noise: -4.714\n",
-      "Iter 34/50 - Loss: -3.187   log_lengthscale: -1.378   log_noise: -4.714\n",
-      "Iter 35/50 - Loss: -7.931   log_lengthscale: -1.382   log_noise: -4.714\n",
-      "Iter 36/50 - Loss: -9.148   log_lengthscale: -1.401   log_noise: -4.714\n",
-      "Iter 37/50 - Loss: -12.666   log_lengthscale: -1.428   log_noise: -4.714\n",
-      "Iter 38/50 - Loss: -14.253   log_lengthscale: -1.459   log_noise: -4.714\n",
-      "Iter 39/50 - Loss: -17.827   log_lengthscale: -1.487   log_noise: -4.714\n",
-      "Iter 40/50 - Loss: -22.998   log_lengthscale: -1.508   log_noise: -4.714\n",
-      "Iter 41/50 - Loss: -26.465   log_lengthscale: -1.521   log_noise: -4.714\n",
-      "Iter 42/50 - Loss: -25.948   log_lengthscale: -1.531   log_noise: -4.714\n",
-      "Iter 43/50 - Loss: -29.126   log_lengthscale: -1.534   log_noise: -4.714\n",
-      "Iter 44/50 - Loss: -33.415   log_lengthscale: -1.535   log_noise: -4.714\n",
-      "Iter 45/50 - Loss: -34.796   log_lengthscale: -1.539   log_noise: -4.714\n",
-      "Iter 46/50 - Loss: -32.216   log_lengthscale: -1.545   log_noise: -4.714\n",
-      "Iter 47/50 - Loss: -34.801   log_lengthscale: -1.554   log_noise: -4.714\n",
-      "Iter 48/50 - Loss: -37.049   log_lengthscale: -1.565   log_noise: -4.714\n",
-      "Iter 49/50 - Loss: -38.725   log_lengthscale: -1.581   log_noise: -4.714\n",
-      "Iter 50/50 - Loss: -44.302   log_lengthscale: -1.599   log_noise: -4.714\n"
+      "Iter 1/50 - Loss: 110.443   log_lengthscale: -0.367   log_noise: -4.714\n",
+      "Iter 2/50 - Loss: 106.103   log_lengthscale: -0.439   log_noise: -4.714\n",
+      "Iter 3/50 - Loss: 103.828   log_lengthscale: -0.514   log_noise: -4.714\n",
+      "Iter 4/50 - Loss: 100.870   log_lengthscale: -0.590   log_noise: -4.714\n",
+      "Iter 5/50 - Loss: 95.405   log_lengthscale: -0.668   log_noise: -4.714\n",
+      "Iter 6/50 - Loss: 91.396   log_lengthscale: -0.747   log_noise: -4.714\n",
+      "Iter 7/50 - Loss: 85.639   log_lengthscale: -0.829   log_noise: -4.714\n",
+      "Iter 8/50 - Loss: 81.592   log_lengthscale: -0.912   log_noise: -4.714\n",
+      "Iter 9/50 - Loss: 79.287   log_lengthscale: -0.996   log_noise: -4.714\n",
+      "Iter 10/50 - Loss: 76.134   log_lengthscale: -1.081   log_noise: -4.714\n",
+      "Iter 11/50 - Loss: 71.860   log_lengthscale: -1.161   log_noise: -4.714\n",
+      "Iter 12/50 - Loss: 68.440   log_lengthscale: -1.232   log_noise: -4.714\n",
+      "Iter 13/50 - Loss: 67.404   log_lengthscale: -1.282   log_noise: -4.714\n",
+      "Iter 14/50 - Loss: 63.705   log_lengthscale: -1.296   log_noise: -4.714\n",
+      "Iter 15/50 - Loss: 59.760   log_lengthscale: -1.289   log_noise: -4.714\n",
+      "Iter 16/50 - Loss: 58.483   log_lengthscale: -1.268   log_noise: -4.714\n",
+      "Iter 17/50 - Loss: 50.841   log_lengthscale: -1.235   log_noise: -4.714\n",
+      "Iter 18/50 - Loss: 46.613   log_lengthscale: -1.197   log_noise: -4.714\n",
+      "Iter 19/50 - Loss: 45.073   log_lengthscale: -1.166   log_noise: -4.714\n",
+      "Iter 20/50 - Loss: 44.561   log_lengthscale: -1.143   log_noise: -4.714\n",
+      "Iter 21/50 - Loss: 36.053   log_lengthscale: -1.129   log_noise: -4.714\n",
+      "Iter 22/50 - Loss: 30.498   log_lengthscale: -1.129   log_noise: -4.714\n",
+      "Iter 23/50 - Loss: 26.863   log_lengthscale: -1.146   log_noise: -4.714\n",
+      "Iter 24/50 - Loss: 25.307   log_lengthscale: -1.176   log_noise: -4.714\n",
+      "Iter 25/50 - Loss: 21.487   log_lengthscale: -1.215   log_noise: -4.714\n",
+      "Iter 26/50 - Loss: 18.100   log_lengthscale: -1.259   log_noise: -4.714\n",
+      "Iter 27/50 - Loss: 18.512   log_lengthscale: -1.295   log_noise: -4.714\n",
+      "Iter 28/50 - Loss: 11.126   log_lengthscale: -1.323   log_noise: -4.714\n",
+      "Iter 29/50 - Loss: 10.259   log_lengthscale: -1.329   log_noise: -4.714\n",
+      "Iter 30/50 - Loss: 5.683   log_lengthscale: -1.325   log_noise: -4.714\n",
+      "Iter 31/50 - Loss: 5.724   log_lengthscale: -1.315   log_noise: -4.714\n",
+      "Iter 32/50 - Loss: -2.059   log_lengthscale: -1.299   log_noise: -4.714\n",
+      "Iter 33/50 - Loss: -7.481   log_lengthscale: -1.283   log_noise: -4.714\n",
+      "Iter 34/50 - Loss: -7.562   log_lengthscale: -1.274   log_noise: -4.714\n",
+      "Iter 35/50 - Loss: -11.460   log_lengthscale: -1.281   log_noise: -4.714\n",
+      "Iter 36/50 - Loss: -16.088   log_lengthscale: -1.296   log_noise: -4.714\n",
+      "Iter 37/50 - Loss: -14.856   log_lengthscale: -1.317   log_noise: -4.714\n",
+      "Iter 38/50 - Loss: -24.291   log_lengthscale: -1.342   log_noise: -4.714\n",
+      "Iter 39/50 - Loss: -21.514   log_lengthscale: -1.365   log_noise: -4.714\n",
+      "Iter 40/50 - Loss: -27.827   log_lengthscale: -1.373   log_noise: -4.714\n",
+      "Iter 41/50 - Loss: -25.034   log_lengthscale: -1.378   log_noise: -4.714\n",
+      "Iter 42/50 - Loss: -24.770   log_lengthscale: -1.379   log_noise: -4.714\n",
+      "Iter 43/50 - Loss: -29.792   log_lengthscale: -1.386   log_noise: -4.714\n",
+      "Iter 44/50 - Loss: -29.995   log_lengthscale: -1.389   log_noise: -4.714\n",
+      "Iter 45/50 - Loss: -31.911   log_lengthscale: -1.385   log_noise: -4.714\n",
+      "Iter 46/50 - Loss: -36.235   log_lengthscale: -1.394   log_noise: -4.714\n",
+      "Iter 47/50 - Loss: -38.634   log_lengthscale: -1.406   log_noise: -4.714\n",
+      "Iter 48/50 - Loss: -38.393   log_lengthscale: -1.414   log_noise: -4.714\n",
+      "Iter 49/50 - Loss: -38.818   log_lengthscale: -1.436   log_noise: -4.714\n",
+      "Iter 50/50 - Loss: -40.494   log_lengthscale: -1.461   log_noise: -4.714\n"
      ]
     }
    ],
@@ -158,19 +158,18 @@
     "# \"Loss\" for GPs - the marginal log likelihood\n",
     "mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)\n",
     "\n",
-    "with gpytorch.settings.max_preconditioner_size(20), gpytorch.settings.max_cg_iterations(50):\n",
-    "    n_iter = 50\n",
-    "    for i in range(n_iter):\n",
-    "        optimizer.zero_grad()\n",
-    "        output = model(train_x)\n",
-    "        loss = -mll(output, train_y)\n",
-    "        loss.backward()\n",
-    "        print('Iter %d/%d - Loss: %.3f   log_lengthscale: %.3f   log_noise: %.3f' % (\n",
-    "            i + 1, n_iter, loss.item(),\n",
-    "            model.covar_module.base_kernel.log_lengthscale.item(),\n",
-    "            model.likelihood.log_noise.item()\n",
-    "        ))\n",
-    "        optimizer.step()"
+    "n_iter = 50\n",
+    "for i in range(n_iter):\n",
+    "    optimizer.zero_grad()\n",
+    "    output = model(train_x)\n",
+    "    loss = -mll(output, train_y)\n",
+    "    loss.backward()\n",
+    "    print('Iter %d/%d - Loss: %.3f   log_lengthscale: %.3f   log_noise: %.3f' % (\n",
+    "        i + 1, n_iter, loss.item(),\n",
+    "        model.covar_module.base_kernel.log_lengthscale.item(),\n",
+    "        model.likelihood.log_noise.item()\n",
+    "    ))\n",
+    "    optimizer.step()"
    ]
   },
   {
@@ -182,12 +181,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1008x720 with 6 Axes>"
       ]
@@ -212,12 +211,12 @@
     "f, dfx, dfy = franke(xv, yv)\n",
     "\n",
     "# Make predictions\n",
-    "with torch.no_grad(), gpytorch.settings.max_preconditioner_size(20), gpytorch.settings.max_cg_iterations(50):\n",
+    "with torch.no_grad(), gpytorch.settings.max_cg_iterations(50):\n",
     "    test_x = torch.stack([xv.reshape(n1*n2, 1), yv.reshape(n1*n2, 1)], -1).squeeze(1)\n",
     "    predictions = likelihood(model(test_x))\n",
     "    mean = predictions.mean\n",
-    "  \n",
-    "extent=(xv.min(), xv.max(), yv.max(), yv.min())\n",
+    "\n",
+    "extent = (xv.min(), xv.max(), yv.max(), yv.min())\n",
     "ax[0, 0].imshow(f, extent=extent, cmap=cm.jet)\n",
     "ax[0, 0].set_title('True values')\n",
     "ax[0, 1].imshow(dfx, extent=extent, cmap=cm.jet)\n",
diff --git a/examples/Simple_GP_Derivative_Example.ipynb b/examples/Simple_GP_Derivative_Example.ipynb
deleted file mode 100644
index e3c403e5d..000000000
--- a/examples/Simple_GP_Derivative_Example.ipynb
+++ /dev/null
@@ -1,236 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import gpytorch\n",
-    "import math\n",
-    "from matplotlib import pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lb, ub = 0.0, 5*math.pi\n",
-    "n = 100\n",
-    "\n",
-    "train_x = torch.linspace(lb, ub, n).unsqueeze(-1) + 0.01*torch.randn(n, 1)\n",
-    "train_y = torch.stack([\n",
-    "    torch.sin(2*train_x) + torch.cos(train_x), \n",
-    "    -torch.sin(train_x) + 2*torch.cos(2*train_x)\n",
-    "], -1).squeeze(1)\n",
-    "\n",
-    "train_y += 0.05 * torch.randn(n, 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class GPModelWithDerivatives(gpytorch.models.ExactGP):\n",
-    "    def __init__(self, train_x, train_y, likelihood):\n",
-    "        super(GPModelWithDerivatives, self).__init__(train_x, train_y, likelihood)\n",
-    "        self.mean_module = gpytorch.means.ConstantMeanGrad()\n",
-    "        self.base_kernel = gpytorch.kernels.RBFKernelGrad()\n",
-    "        self.covar_module = gpytorch.kernels.ScaleKernel(self.base_kernel)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        mean_x = self.mean_module(x)\n",
-    "        covar_x = self.covar_module(x)\n",
-    "        return gpytorch.distributions.MultitaskMultivariateNormal(mean_x, covar_x)\n",
-    "\n",
-    "likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihood(num_tasks=2)\n",
-    "likelihood.initialize(noise=0.01*train_y.std())\n",
-    "likelihood.raw_noise.requires_grad = False\n",
-    "model = GPModelWithDerivatives(train_x, train_y, likelihood)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iter 1/50 - Loss: 116.972   log_lengthscale: -0.367   log_noise: -4.275\n",
-      "Iter 2/50 - Loss: 113.191   log_lengthscale: -0.295   log_noise: -4.275\n",
-      "Iter 3/50 - Loss: 108.608   log_lengthscale: -0.226   log_noise: -4.275\n",
-      "Iter 4/50 - Loss: 103.804   log_lengthscale: -0.160   log_noise: -4.275\n",
-      "Iter 5/50 - Loss: 99.711   log_lengthscale: -0.098   log_noise: -4.275\n",
-      "Iter 6/50 - Loss: 96.265   log_lengthscale: -0.044   log_noise: -4.275\n",
-      "Iter 7/50 - Loss: 91.340   log_lengthscale: 0.003   log_noise: -4.275\n",
-      "Iter 8/50 - Loss: 87.705   log_lengthscale: 0.034   log_noise: -4.275\n",
-      "Iter 9/50 - Loss: 83.479   log_lengthscale: 0.053   log_noise: -4.275\n",
-      "Iter 10/50 - Loss: 79.445   log_lengthscale: 0.060   log_noise: -4.275\n",
-      "Iter 11/50 - Loss: 74.971   log_lengthscale: 0.063   log_noise: -4.275\n",
-      "Iter 12/50 - Loss: 70.757   log_lengthscale: 0.059   log_noise: -4.275\n",
-      "Iter 13/50 - Loss: 66.392   log_lengthscale: 0.051   log_noise: -4.275\n",
-      "Iter 14/50 - Loss: 62.632   log_lengthscale: 0.040   log_noise: -4.275\n",
-      "Iter 15/50 - Loss: 57.697   log_lengthscale: 0.035   log_noise: -4.275\n",
-      "Iter 16/50 - Loss: 53.429   log_lengthscale: 0.033   log_noise: -4.275\n",
-      "Iter 17/50 - Loss: 49.023   log_lengthscale: 0.037   log_noise: -4.275\n",
-      "Iter 18/50 - Loss: 44.555   log_lengthscale: 0.047   log_noise: -4.275\n",
-      "Iter 19/50 - Loss: 40.325   log_lengthscale: 0.058   log_noise: -4.275\n",
-      "Iter 20/50 - Loss: 36.155   log_lengthscale: 0.073   log_noise: -4.275\n",
-      "Iter 21/50 - Loss: 31.492   log_lengthscale: 0.092   log_noise: -4.275\n",
-      "Iter 22/50 - Loss: 27.208   log_lengthscale: 0.107   log_noise: -4.275\n",
-      "Iter 23/50 - Loss: 23.525   log_lengthscale: 0.122   log_noise: -4.275\n",
-      "Iter 24/50 - Loss: 19.104   log_lengthscale: 0.138   log_noise: -4.275\n",
-      "Iter 25/50 - Loss: 14.698   log_lengthscale: 0.153   log_noise: -4.275\n",
-      "Iter 26/50 - Loss: 10.615   log_lengthscale: 0.164   log_noise: -4.275\n",
-      "Iter 27/50 - Loss: 6.340   log_lengthscale: 0.169   log_noise: -4.275\n",
-      "Iter 28/50 - Loss: 2.735   log_lengthscale: 0.169   log_noise: -4.275\n",
-      "Iter 29/50 - Loss: -1.250   log_lengthscale: 0.166   log_noise: -4.275\n",
-      "Iter 30/50 - Loss: -5.139   log_lengthscale: 0.159   log_noise: -4.275\n",
-      "Iter 31/50 - Loss: -8.651   log_lengthscale: 0.156   log_noise: -4.275\n",
-      "Iter 32/50 - Loss: -12.557   log_lengthscale: 0.155   log_noise: -4.275\n",
-      "Iter 33/50 - Loss: -16.334   log_lengthscale: 0.158   log_noise: -4.275\n",
-      "Iter 34/50 - Loss: -19.513   log_lengthscale: 0.166   log_noise: -4.275\n",
-      "Iter 35/50 - Loss: -22.231   log_lengthscale: 0.172   log_noise: -4.275\n",
-      "Iter 36/50 - Loss: -25.844   log_lengthscale: 0.176   log_noise: -4.275\n",
-      "Iter 37/50 - Loss: -28.167   log_lengthscale: 0.181   log_noise: -4.275\n",
-      "Iter 38/50 - Loss: -31.349   log_lengthscale: 0.190   log_noise: -4.275\n",
-      "Iter 39/50 - Loss: -34.065   log_lengthscale: 0.202   log_noise: -4.275\n",
-      "Iter 40/50 - Loss: -36.747   log_lengthscale: 0.214   log_noise: -4.275\n",
-      "Iter 41/50 - Loss: -39.227   log_lengthscale: 0.221   log_noise: -4.275\n",
-      "Iter 42/50 - Loss: -41.299   log_lengthscale: 0.219   log_noise: -4.275\n",
-      "Iter 43/50 - Loss: -43.466   log_lengthscale: 0.212   log_noise: -4.275\n",
-      "Iter 44/50 - Loss: -45.630   log_lengthscale: 0.206   log_noise: -4.275\n",
-      "Iter 45/50 - Loss: -47.610   log_lengthscale: 0.202   log_noise: -4.275\n",
-      "Iter 46/50 - Loss: -49.196   log_lengthscale: 0.199   log_noise: -4.275\n",
-      "Iter 47/50 - Loss: -50.895   log_lengthscale: 0.195   log_noise: -4.275\n",
-      "Iter 48/50 - Loss: -52.518   log_lengthscale: 0.196   log_noise: -4.275\n",
-      "Iter 49/50 - Loss: -54.058   log_lengthscale: 0.204   log_noise: -4.275\n",
-      "Iter 50/50 - Loss: -55.310   log_lengthscale: 0.215   log_noise: -4.275\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Find optimal model hyperparameters\n",
-    "model.train()\n",
-    "likelihood.train()\n",
-    "\n",
-    "# Use the adam optimizer\n",
-    "optimizer = torch.optim.Adam([\n",
-    "    {'params': model.parameters()},  # Includes GaussianLikelihood parameters\n",
-    "], lr=0.1)\n",
-    "\n",
-    "# \"Loss\" for GPs - the marginal log likelihood\n",
-    "mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)\n",
-    "\n",
-    "with gpytorch.settings.max_preconditioner_size(20), gpytorch.settings.max_cg_iterations(50):\n",
-    "    n_iter = 50\n",
-    "    for i in range(n_iter):\n",
-    "        optimizer.zero_grad()\n",
-    "        output = model(train_x)\n",
-    "        loss = -mll(output, train_y)\n",
-    "        loss.backward()\n",
-    "        print('Iter %d/%d - Loss: %.3f   log_lengthscale: %.3f   log_noise: %.3f' % (\n",
-    "            i + 1, n_iter, loss.item(),\n",
-    "            model.covar_module.base_kernel.log_lengthscale.item(),\n",
-    "            model.likelihood.log_noise.item()\n",
-    "        ))        \n",
-    "        optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Predicting "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set into eval mode\n",
-    "model.train()\n",
-    "model.eval()\n",
-    "likelihood.eval()\n",
-    "\n",
-    "# Initialize plots\n",
-    "f, (y1_ax, y2_ax) = plt.subplots(1, 2, figsize=(12, 6))\n",
-    "\n",
-    "# Make predictions\n",
-    "with torch.no_grad(), gpytorch.settings.max_preconditioner_size(20), gpytorch.settings.max_cg_iterations(50):\n",
-    "    test_x = torch.linspace(lb, ub, 500)\n",
-    "    predictions = likelihood(model(test_x))\n",
-    "    mean = predictions.mean\n",
-    "    lower, upper = predictions.confidence_region()\n",
-    "    \n",
-    "# Plot training data as black stars\n",
-    "y1_ax.plot(train_x.detach().numpy(), train_y[:, 0].detach().numpy(), 'k*')\n",
-    "# Predictive mean as blue line\n",
-    "y1_ax.plot(test_x.numpy(), mean[:, 0].numpy(), 'b')\n",
-    "# Shade in confidence \n",
-    "y1_ax.fill_between(test_x.numpy(), lower[:, 0].numpy(), upper[:, 0].numpy(), alpha=0.5)\n",
-    "y1_ax.legend(['Observed Values', 'Mean', 'Confidence'])\n",
-    "y1_ax.set_title('Function values')\n",
-    "\n",
-    "# Plot training data as black stars\n",
-    "y2_ax.plot(train_x.detach().numpy(), train_y[:, 1].detach().numpy(), 'k*')\n",
-    "# Predictive mean as blue line\n",
-    "y2_ax.plot(test_x.numpy(), mean[:, 1].numpy(), 'b')\n",
-    "# Shade in confidence \n",
-    "y2_ax.fill_between(test_x.numpy(), lower[:, 1].numpy(), upper[:, 1].numpy(), alpha=0.5)\n",
-    "y2_ax.legend(['Observed Derivatives', 'Mean', 'Confidence'])\n",
-    "y2_ax.set_title('Derivatives')\n",
-    "\n",
-    "None"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/gpytorch/kernels/rbf_kernel_grad.py b/gpytorch/kernels/rbf_kernel_grad.py
index 3aea25c75..7157b52d0 100644
--- a/gpytorch/kernels/rbf_kernel_grad.py
+++ b/gpytorch/kernels/rbf_kernel_grad.py
@@ -72,14 +72,17 @@ def forward(self, x1, x2, diag=False, **params):
             fact1 = kp.evaluate() / ell.pow(2) - outer3 / ell.pow(2)
             K[..., n1:, n2:] = fact1 * K_11.repeat(shape_list + [d, d]) 
 
-            if n1 == n2 and torch.eq(x1, x2).all():  # Symmetrize for stability
+            # Symmetrize for stability
+            if n1 == n2 and torch.eq(x1, x2).all():  
                 K = 0.5*(K.transpose(-1, -2) + K)
 
+            # Apply a perfect shuffle permutation to match the MutiTask ordering
             pi1 = torch.arange(n1 * (d + 1)).view(d + 1, n1).t().contiguous().view((n1 * (d + 1)))
             pi2 = torch.arange(n2 * (d + 1)).view(d + 1, n2).t().contiguous().view((n2 * (d + 1)))
             K = K[..., pi1, :][..., :, pi2]
 
             return K
+            
         else: # TODO: This will change when ARD is supported
             kernel_diag = super(RBFKernelGrad, self).forward(x1, x2, diag=True)
             grad_diag = (1 / self.lengthscale.item() ** 2) * torch.ones(1, n2*d)

From 556baff7e0d8e34d931cdd1a1032a704f71b15ce Mon Sep 17 00:00:00 2001
From: dme65 <dme65@cornell.edu>
Date: Thu, 10 Jan 2019 16:17:40 -0500
Subject: [PATCH 08/25] Adding tests + cleaning up batch handling

---
 ...Regression_Derivative_Information_1d.ipynb | 102 +++----
 ...Regression_Derivative_Information_2d.ipynb | 102 +++----
 .../Simple_GP_Derivative_Example-2d.ipynb     | 266 ------------------
 gpytorch/kernels/rbf_kernel_grad.py           |  32 ++-
 test/kernels/test_rbf_kernel_grad.py          |  56 ++++
 test/means/test_constant_mean_grad.py         |  25 ++
 6 files changed, 201 insertions(+), 382 deletions(-)
 delete mode 100644 examples/Simple_GP_Derivative_Example-2d.ipynb
 create mode 100644 test/kernels/test_rbf_kernel_grad.py
 create mode 100644 test/means/test_constant_mean_grad.py

diff --git a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb
index 1a82f2f94..78b3f8d6e 100644
--- a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb
+++ b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb
@@ -112,56 +112,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iter 1/50 - Loss: 61.807   log_lengthscale: -0.367   log_noise: -4.275\n",
-      "Iter 2/50 - Loss: 59.101   log_lengthscale: -0.295   log_noise: -4.275\n",
-      "Iter 3/50 - Loss: 57.529   log_lengthscale: -0.228   log_noise: -4.275\n",
-      "Iter 4/50 - Loss: 54.055   log_lengthscale: -0.162   log_noise: -4.275\n",
-      "Iter 5/50 - Loss: 53.405   log_lengthscale: -0.100   log_noise: -4.275\n",
-      "Iter 6/50 - Loss: 51.161   log_lengthscale: -0.049   log_noise: -4.275\n",
-      "Iter 7/50 - Loss: 50.384   log_lengthscale: -0.006   log_noise: -4.275\n",
-      "Iter 8/50 - Loss: 48.640   log_lengthscale: 0.023   log_noise: -4.275\n",
-      "Iter 9/50 - Loss: 46.084   log_lengthscale: 0.038   log_noise: -4.275\n",
-      "Iter 10/50 - Loss: 44.910   log_lengthscale: 0.044   log_noise: -4.275\n",
-      "Iter 11/50 - Loss: 42.824   log_lengthscale: 0.042   log_noise: -4.275\n",
-      "Iter 12/50 - Loss: 41.261   log_lengthscale: 0.034   log_noise: -4.275\n",
-      "Iter 13/50 - Loss: 37.953   log_lengthscale: 0.023   log_noise: -4.275\n",
-      "Iter 14/50 - Loss: 34.888   log_lengthscale: 0.014   log_noise: -4.275\n",
-      "Iter 15/50 - Loss: 32.862   log_lengthscale: 0.007   log_noise: -4.275\n",
-      "Iter 16/50 - Loss: 35.709   log_lengthscale: 0.001   log_noise: -4.275\n",
-      "Iter 17/50 - Loss: 30.031   log_lengthscale: 0.007   log_noise: -4.275\n",
-      "Iter 18/50 - Loss: 28.395   log_lengthscale: 0.016   log_noise: -4.275\n",
-      "Iter 19/50 - Loss: 26.709   log_lengthscale: 0.027   log_noise: -4.275\n",
-      "Iter 20/50 - Loss: 25.436   log_lengthscale: 0.043   log_noise: -4.275\n",
-      "Iter 21/50 - Loss: 25.401   log_lengthscale: 0.064   log_noise: -4.275\n",
-      "Iter 22/50 - Loss: 21.223   log_lengthscale: 0.088   log_noise: -4.275\n",
-      "Iter 23/50 - Loss: 18.429   log_lengthscale: 0.111   log_noise: -4.275\n",
-      "Iter 24/50 - Loss: 21.253   log_lengthscale: 0.128   log_noise: -4.275\n",
-      "Iter 25/50 - Loss: 16.943   log_lengthscale: 0.142   log_noise: -4.275\n",
-      "Iter 26/50 - Loss: 16.183   log_lengthscale: 0.155   log_noise: -4.275\n",
-      "Iter 27/50 - Loss: 14.646   log_lengthscale: 0.164   log_noise: -4.275\n",
-      "Iter 28/50 - Loss: 11.569   log_lengthscale: 0.168   log_noise: -4.275\n",
-      "Iter 29/50 - Loss: 9.167   log_lengthscale: 0.167   log_noise: -4.275\n",
-      "Iter 30/50 - Loss: 7.116   log_lengthscale: 0.170   log_noise: -4.275\n",
-      "Iter 31/50 - Loss: 9.166   log_lengthscale: 0.170   log_noise: -4.275\n",
-      "Iter 32/50 - Loss: 5.869   log_lengthscale: 0.171   log_noise: -4.275\n",
-      "Iter 33/50 - Loss: 4.133   log_lengthscale: 0.174   log_noise: -4.275\n",
-      "Iter 34/50 - Loss: -0.490   log_lengthscale: 0.177   log_noise: -4.275\n",
-      "Iter 35/50 - Loss: -2.021   log_lengthscale: 0.180   log_noise: -4.275\n",
-      "Iter 36/50 - Loss: 3.194   log_lengthscale: 0.180   log_noise: -4.275\n",
-      "Iter 37/50 - Loss: -2.839   log_lengthscale: 0.189   log_noise: -4.275\n",
-      "Iter 38/50 - Loss: -3.883   log_lengthscale: 0.197   log_noise: -4.275\n",
-      "Iter 39/50 - Loss: -3.484   log_lengthscale: 0.205   log_noise: -4.275\n",
-      "Iter 40/50 - Loss: -6.994   log_lengthscale: 0.217   log_noise: -4.275\n",
-      "Iter 41/50 - Loss: -3.418   log_lengthscale: 0.222   log_noise: -4.275\n",
-      "Iter 42/50 - Loss: -1.686   log_lengthscale: 0.222   log_noise: -4.275\n",
-      "Iter 43/50 - Loss: -8.598   log_lengthscale: 0.233   log_noise: -4.275\n",
-      "Iter 44/50 - Loss: -11.501   log_lengthscale: 0.241   log_noise: -4.275\n",
-      "Iter 45/50 - Loss: -8.583   log_lengthscale: 0.241   log_noise: -4.275\n",
-      "Iter 46/50 - Loss: -9.892   log_lengthscale: 0.239   log_noise: -4.275\n",
-      "Iter 47/50 - Loss: -14.409   log_lengthscale: 0.235   log_noise: -4.275\n",
-      "Iter 48/50 - Loss: -10.353   log_lengthscale: 0.228   log_noise: -4.275\n",
-      "Iter 49/50 - Loss: -11.754   log_lengthscale: 0.224   log_noise: -4.275\n",
-      "Iter 50/50 - Loss: -13.165   log_lengthscale: 0.228   log_noise: -4.275\n"
+      "Iter 1/50 - Loss: 60.730   log_lengthscale: -0.367   log_noise: -4.265\n",
+      "Iter 2/50 - Loss: 59.618   log_lengthscale: -0.295   log_noise: -4.265\n",
+      "Iter 3/50 - Loss: 57.693   log_lengthscale: -0.225   log_noise: -4.265\n",
+      "Iter 4/50 - Loss: 55.330   log_lengthscale: -0.158   log_noise: -4.265\n",
+      "Iter 5/50 - Loss: 52.936   log_lengthscale: -0.096   log_noise: -4.265\n",
+      "Iter 6/50 - Loss: 52.683   log_lengthscale: -0.043   log_noise: -4.265\n",
+      "Iter 7/50 - Loss: 48.544   log_lengthscale: -0.005   log_noise: -4.265\n",
+      "Iter 8/50 - Loss: 49.339   log_lengthscale: 0.020   log_noise: -4.265\n",
+      "Iter 9/50 - Loss: 46.650   log_lengthscale: 0.035   log_noise: -4.265\n",
+      "Iter 10/50 - Loss: 46.521   log_lengthscale: 0.039   log_noise: -4.265\n",
+      "Iter 11/50 - Loss: 43.292   log_lengthscale: 0.038   log_noise: -4.265\n",
+      "Iter 12/50 - Loss: 40.677   log_lengthscale: 0.029   log_noise: -4.265\n",
+      "Iter 13/50 - Loss: 39.721   log_lengthscale: 0.019   log_noise: -4.265\n",
+      "Iter 14/50 - Loss: 37.023   log_lengthscale: 0.015   log_noise: -4.265\n",
+      "Iter 15/50 - Loss: 37.851   log_lengthscale: 0.018   log_noise: -4.265\n",
+      "Iter 16/50 - Loss: 31.790   log_lengthscale: 0.028   log_noise: -4.265\n",
+      "Iter 17/50 - Loss: 32.158   log_lengthscale: 0.037   log_noise: -4.265\n",
+      "Iter 18/50 - Loss: 31.662   log_lengthscale: 0.049   log_noise: -4.265\n",
+      "Iter 19/50 - Loss: 27.468   log_lengthscale: 0.065   log_noise: -4.265\n",
+      "Iter 20/50 - Loss: 23.470   log_lengthscale: 0.077   log_noise: -4.265\n",
+      "Iter 21/50 - Loss: 22.215   log_lengthscale: 0.086   log_noise: -4.265\n",
+      "Iter 22/50 - Loss: 21.394   log_lengthscale: 0.096   log_noise: -4.265\n",
+      "Iter 23/50 - Loss: 20.012   log_lengthscale: 0.104   log_noise: -4.265\n",
+      "Iter 24/50 - Loss: 17.207   log_lengthscale: 0.110   log_noise: -4.265\n",
+      "Iter 25/50 - Loss: 18.864   log_lengthscale: 0.115   log_noise: -4.265\n",
+      "Iter 26/50 - Loss: 12.822   log_lengthscale: 0.119   log_noise: -4.265\n",
+      "Iter 27/50 - Loss: 14.679   log_lengthscale: 0.123   log_noise: -4.265\n",
+      "Iter 28/50 - Loss: 12.624   log_lengthscale: 0.133   log_noise: -4.265\n",
+      "Iter 29/50 - Loss: 12.586   log_lengthscale: 0.146   log_noise: -4.265\n",
+      "Iter 30/50 - Loss: 9.183   log_lengthscale: 0.161   log_noise: -4.265\n",
+      "Iter 31/50 - Loss: 7.975   log_lengthscale: 0.176   log_noise: -4.265\n",
+      "Iter 32/50 - Loss: 10.570   log_lengthscale: 0.186   log_noise: -4.265\n",
+      "Iter 33/50 - Loss: 1.626   log_lengthscale: 0.194   log_noise: -4.265\n",
+      "Iter 34/50 - Loss: 3.866   log_lengthscale: 0.195   log_noise: -4.265\n",
+      "Iter 35/50 - Loss: 0.209   log_lengthscale: 0.189   log_noise: -4.265\n",
+      "Iter 36/50 - Loss: -0.803   log_lengthscale: 0.180   log_noise: -4.265\n",
+      "Iter 37/50 - Loss: 0.293   log_lengthscale: 0.173   log_noise: -4.265\n",
+      "Iter 38/50 - Loss: -3.192   log_lengthscale: 0.171   log_noise: -4.265\n",
+      "Iter 39/50 - Loss: -3.721   log_lengthscale: 0.178   log_noise: -4.265\n",
+      "Iter 40/50 - Loss: -6.172   log_lengthscale: 0.190   log_noise: -4.265\n",
+      "Iter 41/50 - Loss: -5.489   log_lengthscale: 0.203   log_noise: -4.265\n",
+      "Iter 42/50 - Loss: -6.737   log_lengthscale: 0.220   log_noise: -4.265\n",
+      "Iter 43/50 - Loss: -8.844   log_lengthscale: 0.225   log_noise: -4.265\n",
+      "Iter 44/50 - Loss: -6.363   log_lengthscale: 0.232   log_noise: -4.265\n",
+      "Iter 45/50 - Loss: -8.511   log_lengthscale: 0.235   log_noise: -4.265\n",
+      "Iter 46/50 - Loss: -9.528   log_lengthscale: 0.238   log_noise: -4.265\n",
+      "Iter 47/50 - Loss: -8.002   log_lengthscale: 0.237   log_noise: -4.265\n",
+      "Iter 48/50 - Loss: -13.198   log_lengthscale: 0.244   log_noise: -4.265\n",
+      "Iter 49/50 - Loss: -15.427   log_lengthscale: 0.244   log_noise: -4.265\n",
+      "Iter 50/50 - Loss: -11.407   log_lengthscale: 0.235   log_noise: -4.265\n"
      ]
     }
    ],
@@ -207,7 +207,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x432 with 2 Axes>"
       ]
diff --git a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb
index a956a6726..421dfced1 100644
--- a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb
+++ b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb
@@ -136,56 +136,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iter 1/50 - Loss: 110.384   log_lengthscale: -0.367   log_noise: -4.714\n",
-      "Iter 2/50 - Loss: 107.250   log_lengthscale: -0.439   log_noise: -4.714\n",
-      "Iter 3/50 - Loss: 103.263   log_lengthscale: -0.514   log_noise: -4.714\n",
-      "Iter 4/50 - Loss: 99.708   log_lengthscale: -0.590   log_noise: -4.714\n",
-      "Iter 5/50 - Loss: 95.151   log_lengthscale: -0.667   log_noise: -4.714\n",
-      "Iter 6/50 - Loss: 90.692   log_lengthscale: -0.747   log_noise: -4.714\n",
-      "Iter 7/50 - Loss: 87.713   log_lengthscale: -0.828   log_noise: -4.714\n",
-      "Iter 8/50 - Loss: 83.105   log_lengthscale: -0.911   log_noise: -4.714\n",
-      "Iter 9/50 - Loss: 79.508   log_lengthscale: -0.994   log_noise: -4.714\n",
-      "Iter 10/50 - Loss: 74.017   log_lengthscale: -1.078   log_noise: -4.714\n",
-      "Iter 11/50 - Loss: 70.958   log_lengthscale: -1.157   log_noise: -4.714\n",
-      "Iter 12/50 - Loss: 71.528   log_lengthscale: -1.224   log_noise: -4.714\n",
-      "Iter 13/50 - Loss: 66.021   log_lengthscale: -1.260   log_noise: -4.714\n",
-      "Iter 14/50 - Loss: 63.496   log_lengthscale: -1.273   log_noise: -4.714\n",
-      "Iter 15/50 - Loss: 59.752   log_lengthscale: -1.266   log_noise: -4.714\n",
-      "Iter 16/50 - Loss: 52.506   log_lengthscale: -1.247   log_noise: -4.714\n",
-      "Iter 17/50 - Loss: 52.274   log_lengthscale: -1.218   log_noise: -4.714\n",
-      "Iter 18/50 - Loss: 45.548   log_lengthscale: -1.187   log_noise: -4.714\n",
-      "Iter 19/50 - Loss: 43.033   log_lengthscale: -1.160   log_noise: -4.714\n",
-      "Iter 20/50 - Loss: 37.667   log_lengthscale: -1.144   log_noise: -4.714\n",
-      "Iter 21/50 - Loss: 34.453   log_lengthscale: -1.146   log_noise: -4.714\n",
-      "Iter 22/50 - Loss: 30.147   log_lengthscale: -1.163   log_noise: -4.714\n",
-      "Iter 23/50 - Loss: 31.184   log_lengthscale: -1.193   log_noise: -4.714\n",
-      "Iter 24/50 - Loss: 22.408   log_lengthscale: -1.232   log_noise: -4.714\n",
-      "Iter 25/50 - Loss: 26.248   log_lengthscale: -1.272   log_noise: -4.714\n",
-      "Iter 26/50 - Loss: 17.774   log_lengthscale: -1.308   log_noise: -4.714\n",
-      "Iter 27/50 - Loss: 14.621   log_lengthscale: -1.333   log_noise: -4.714\n",
-      "Iter 28/50 - Loss: 11.387   log_lengthscale: -1.346   log_noise: -4.714\n",
-      "Iter 29/50 - Loss: 11.609   log_lengthscale: -1.356   log_noise: -4.714\n",
-      "Iter 30/50 - Loss: 7.574   log_lengthscale: -1.354   log_noise: -4.714\n",
-      "Iter 31/50 - Loss: 4.772   log_lengthscale: -1.344   log_noise: -4.714\n",
-      "Iter 32/50 - Loss: 1.492   log_lengthscale: -1.326   log_noise: -4.714\n",
-      "Iter 33/50 - Loss: -3.760   log_lengthscale: -1.301   log_noise: -4.714\n",
-      "Iter 34/50 - Loss: -3.007   log_lengthscale: -1.292   log_noise: -4.714\n",
-      "Iter 35/50 - Loss: -8.198   log_lengthscale: -1.299   log_noise: -4.714\n",
-      "Iter 36/50 - Loss: -15.006   log_lengthscale: -1.317   log_noise: -4.714\n",
-      "Iter 37/50 - Loss: -11.899   log_lengthscale: -1.353   log_noise: -4.714\n",
-      "Iter 38/50 - Loss: -19.430   log_lengthscale: -1.397   log_noise: -4.714\n",
-      "Iter 39/50 - Loss: -18.595   log_lengthscale: -1.443   log_noise: -4.714\n",
-      "Iter 40/50 - Loss: -19.237   log_lengthscale: -1.472   log_noise: -4.714\n",
-      "Iter 41/50 - Loss: -23.092   log_lengthscale: -1.495   log_noise: -4.714\n",
-      "Iter 42/50 - Loss: -22.628   log_lengthscale: -1.499   log_noise: -4.714\n",
-      "Iter 43/50 - Loss: -23.558   log_lengthscale: -1.497   log_noise: -4.714\n",
-      "Iter 44/50 - Loss: -26.297   log_lengthscale: -1.487   log_noise: -4.714\n",
-      "Iter 45/50 - Loss: -32.775   log_lengthscale: -1.480   log_noise: -4.714\n",
-      "Iter 46/50 - Loss: -28.115   log_lengthscale: -1.480   log_noise: -4.714\n",
-      "Iter 47/50 - Loss: -28.947   log_lengthscale: -1.488   log_noise: -4.714\n",
-      "Iter 48/50 - Loss: -36.858   log_lengthscale: -1.501   log_noise: -4.714\n",
-      "Iter 49/50 - Loss: -36.496   log_lengthscale: -1.522   log_noise: -4.714\n",
-      "Iter 50/50 - Loss: -37.434   log_lengthscale: -1.548   log_noise: -4.714\n"
+      "Iter 1/50 - Loss: 110.809   log_lengthscale: -0.367   log_noise: -4.712\n",
+      "Iter 2/50 - Loss: 107.447   log_lengthscale: -0.439   log_noise: -4.712\n",
+      "Iter 3/50 - Loss: 104.017   log_lengthscale: -0.514   log_noise: -4.712\n",
+      "Iter 4/50 - Loss: 100.469   log_lengthscale: -0.590   log_noise: -4.712\n",
+      "Iter 5/50 - Loss: 95.480   log_lengthscale: -0.668   log_noise: -4.712\n",
+      "Iter 6/50 - Loss: 94.004   log_lengthscale: -0.748   log_noise: -4.712\n",
+      "Iter 7/50 - Loss: 86.962   log_lengthscale: -0.829   log_noise: -4.712\n",
+      "Iter 8/50 - Loss: 80.542   log_lengthscale: -0.912   log_noise: -4.712\n",
+      "Iter 9/50 - Loss: 81.405   log_lengthscale: -0.997   log_noise: -4.712\n",
+      "Iter 10/50 - Loss: 73.289   log_lengthscale: -1.082   log_noise: -4.712\n",
+      "Iter 11/50 - Loss: 74.072   log_lengthscale: -1.165   log_noise: -4.712\n",
+      "Iter 12/50 - Loss: 73.329   log_lengthscale: -1.232   log_noise: -4.712\n",
+      "Iter 13/50 - Loss: 68.476   log_lengthscale: -1.275   log_noise: -4.712\n",
+      "Iter 14/50 - Loss: 63.706   log_lengthscale: -1.296   log_noise: -4.712\n",
+      "Iter 15/50 - Loss: 63.301   log_lengthscale: -1.291   log_noise: -4.712\n",
+      "Iter 16/50 - Loss: 57.303   log_lengthscale: -1.266   log_noise: -4.712\n",
+      "Iter 17/50 - Loss: 54.499   log_lengthscale: -1.231   log_noise: -4.712\n",
+      "Iter 18/50 - Loss: 49.428   log_lengthscale: -1.193   log_noise: -4.712\n",
+      "Iter 19/50 - Loss: 41.405   log_lengthscale: -1.153   log_noise: -4.712\n",
+      "Iter 20/50 - Loss: 36.237   log_lengthscale: -1.130   log_noise: -4.712\n",
+      "Iter 21/50 - Loss: 35.812   log_lengthscale: -1.127   log_noise: -4.712\n",
+      "Iter 22/50 - Loss: 32.704   log_lengthscale: -1.142   log_noise: -4.712\n",
+      "Iter 23/50 - Loss: 29.561   log_lengthscale: -1.168   log_noise: -4.712\n",
+      "Iter 24/50 - Loss: 29.646   log_lengthscale: -1.205   log_noise: -4.712\n",
+      "Iter 25/50 - Loss: 22.450   log_lengthscale: -1.247   log_noise: -4.712\n",
+      "Iter 26/50 - Loss: 23.551   log_lengthscale: -1.292   log_noise: -4.712\n",
+      "Iter 27/50 - Loss: 16.627   log_lengthscale: -1.323   log_noise: -4.712\n",
+      "Iter 28/50 - Loss: 11.997   log_lengthscale: -1.340   log_noise: -4.712\n",
+      "Iter 29/50 - Loss: 14.768   log_lengthscale: -1.351   log_noise: -4.712\n",
+      "Iter 30/50 - Loss: 10.343   log_lengthscale: -1.344   log_noise: -4.712\n",
+      "Iter 31/50 - Loss: 6.235   log_lengthscale: -1.329   log_noise: -4.712\n",
+      "Iter 32/50 - Loss: 1.952   log_lengthscale: -1.307   log_noise: -4.712\n",
+      "Iter 33/50 - Loss: -0.172   log_lengthscale: -1.285   log_noise: -4.712\n",
+      "Iter 34/50 - Loss: -5.733   log_lengthscale: -1.267   log_noise: -4.712\n",
+      "Iter 35/50 - Loss: -0.967   log_lengthscale: -1.268   log_noise: -4.712\n",
+      "Iter 36/50 - Loss: -12.498   log_lengthscale: -1.286   log_noise: -4.712\n",
+      "Iter 37/50 - Loss: -14.981   log_lengthscale: -1.319   log_noise: -4.712\n",
+      "Iter 38/50 - Loss: -16.068   log_lengthscale: -1.358   log_noise: -4.712\n",
+      "Iter 39/50 - Loss: -20.499   log_lengthscale: -1.396   log_noise: -4.712\n",
+      "Iter 40/50 - Loss: -11.844   log_lengthscale: -1.426   log_noise: -4.712\n",
+      "Iter 41/50 - Loss: -19.826   log_lengthscale: -1.444   log_noise: -4.712\n",
+      "Iter 42/50 - Loss: -15.652   log_lengthscale: -1.448   log_noise: -4.712\n",
+      "Iter 43/50 - Loss: -26.009   log_lengthscale: -1.435   log_noise: -4.712\n",
+      "Iter 44/50 - Loss: -31.040   log_lengthscale: -1.422   log_noise: -4.712\n",
+      "Iter 45/50 - Loss: -25.432   log_lengthscale: -1.408   log_noise: -4.712\n",
+      "Iter 46/50 - Loss: -34.473   log_lengthscale: -1.398   log_noise: -4.712\n",
+      "Iter 47/50 - Loss: -35.418   log_lengthscale: -1.403   log_noise: -4.712\n",
+      "Iter 48/50 - Loss: -34.949   log_lengthscale: -1.425   log_noise: -4.712\n",
+      "Iter 49/50 - Loss: -35.762   log_lengthscale: -1.455   log_noise: -4.712\n",
+      "Iter 50/50 - Loss: -36.127   log_lengthscale: -1.487   log_noise: -4.712\n"
      ]
     }
    ],
@@ -231,7 +231,7 @@
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 1008x720 with 6 Axes>"
       ]
diff --git a/examples/Simple_GP_Derivative_Example-2d.ipynb b/examples/Simple_GP_Derivative_Example-2d.ipynb
deleted file mode 100644
index 2749027f3..000000000
--- a/examples/Simple_GP_Derivative_Example-2d.ipynb
+++ /dev/null
@@ -1,266 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import gpytorch\n",
-    "import math\n",
-    "from matplotlib import cm\n",
-    "from matplotlib import pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "%matplotlib inline\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def franke(X, Y):\n",
-    "    term1 = .75*torch.exp(-((9*X - 2).pow(2) + (9*Y - 2).pow(2)) / 4)\n",
-    "    term2 = .75*torch.exp(-((9*X + 1).pow(2)) / 49 - (9*Y + 1) / 10)\n",
-    "    term3 = .5*torch.exp(-((9*X - 7).pow(2) + (9*Y - 3).pow(2)) / 4)\n",
-    "    term4 = .2*torch.exp(-(9*X - 4).pow(2) - (9*Y - 7).pow(2))\n",
-    "    \n",
-    "    f = term1 + term2 + term3 - term4\n",
-    "    dfx = -2*(9*X - 2)*9/4 * term1 - 2*(9*X + 1)*9/49 * term2 + \\\n",
-    "          -2*(9*X - 7)*9/4 * term3 + 2*(9*X - 4)*9 * term4\n",
-    "    dfy = -2*(9*Y - 2)*9/4 * term1 - 9/10 * term2 + \\\n",
-    "          -2*(9*Y - 3)*9/4 * term3 + 2*(9*Y - 7)*9 * term4\n",
-    "    \n",
-    "    return f, dfx, dfy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xv, yv = torch.meshgrid([torch.linspace(0, 1, 10), torch.linspace(0, 1, 10)])\n",
-    "train_x = torch.cat((\n",
-    "    xv.contiguous().view(xv.numel(), 1), \n",
-    "    yv.contiguous().view(yv.numel(), 1)),\n",
-    "    dim=1\n",
-    ")\n",
-    "\n",
-    "f, dfx, dfy = franke(train_x[:, 0], train_x[:, 1])\n",
-    "train_y = torch.stack([f, dfx, dfy], -1).squeeze(1)\n",
-    "train_y += 0.01 * torch.randn(train_y.size())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "class GPModelWithDerivatives(gpytorch.models.ExactGP):\n",
-    "    def __init__(self, train_x, train_y, likelihood):\n",
-    "        super(GPModelWithDerivatives, self).__init__(train_x, train_y, likelihood)\n",
-    "        self.mean_module = gpytorch.means.ConstantMeanGrad()\n",
-    "        self.base_kernel = gpytorch.kernels.RBFKernelGrad()\n",
-    "        self.covar_module = gpytorch.kernels.ScaleKernel(self.base_kernel)\n",
-    "        \n",
-    "    def forward(self, x):\n",
-    "        mean_x = self.mean_module(x)\n",
-    "        covar_x = self.covar_module(x)\n",
-    "        return gpytorch.distributions.MultitaskMultivariateNormal(mean_x, covar_x)\n",
-    "\n",
-    "likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihood(num_tasks=3)\n",
-    "likelihood.initialize(noise=0.01*train_y.std())  # Require at least 1% noise (approximately)\n",
-    "likelihood.raw_noise.requires_grad = False\n",
-    "model = GPModelWithDerivatives(train_x, train_y, likelihood)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iter 1/50 - Loss: 110.443   log_lengthscale: -0.367   log_noise: -4.714\n",
-      "Iter 2/50 - Loss: 106.103   log_lengthscale: -0.439   log_noise: -4.714\n",
-      "Iter 3/50 - Loss: 103.828   log_lengthscale: -0.514   log_noise: -4.714\n",
-      "Iter 4/50 - Loss: 100.870   log_lengthscale: -0.590   log_noise: -4.714\n",
-      "Iter 5/50 - Loss: 95.405   log_lengthscale: -0.668   log_noise: -4.714\n",
-      "Iter 6/50 - Loss: 91.396   log_lengthscale: -0.747   log_noise: -4.714\n",
-      "Iter 7/50 - Loss: 85.639   log_lengthscale: -0.829   log_noise: -4.714\n",
-      "Iter 8/50 - Loss: 81.592   log_lengthscale: -0.912   log_noise: -4.714\n",
-      "Iter 9/50 - Loss: 79.287   log_lengthscale: -0.996   log_noise: -4.714\n",
-      "Iter 10/50 - Loss: 76.134   log_lengthscale: -1.081   log_noise: -4.714\n",
-      "Iter 11/50 - Loss: 71.860   log_lengthscale: -1.161   log_noise: -4.714\n",
-      "Iter 12/50 - Loss: 68.440   log_lengthscale: -1.232   log_noise: -4.714\n",
-      "Iter 13/50 - Loss: 67.404   log_lengthscale: -1.282   log_noise: -4.714\n",
-      "Iter 14/50 - Loss: 63.705   log_lengthscale: -1.296   log_noise: -4.714\n",
-      "Iter 15/50 - Loss: 59.760   log_lengthscale: -1.289   log_noise: -4.714\n",
-      "Iter 16/50 - Loss: 58.483   log_lengthscale: -1.268   log_noise: -4.714\n",
-      "Iter 17/50 - Loss: 50.841   log_lengthscale: -1.235   log_noise: -4.714\n",
-      "Iter 18/50 - Loss: 46.613   log_lengthscale: -1.197   log_noise: -4.714\n",
-      "Iter 19/50 - Loss: 45.073   log_lengthscale: -1.166   log_noise: -4.714\n",
-      "Iter 20/50 - Loss: 44.561   log_lengthscale: -1.143   log_noise: -4.714\n",
-      "Iter 21/50 - Loss: 36.053   log_lengthscale: -1.129   log_noise: -4.714\n",
-      "Iter 22/50 - Loss: 30.498   log_lengthscale: -1.129   log_noise: -4.714\n",
-      "Iter 23/50 - Loss: 26.863   log_lengthscale: -1.146   log_noise: -4.714\n",
-      "Iter 24/50 - Loss: 25.307   log_lengthscale: -1.176   log_noise: -4.714\n",
-      "Iter 25/50 - Loss: 21.487   log_lengthscale: -1.215   log_noise: -4.714\n",
-      "Iter 26/50 - Loss: 18.100   log_lengthscale: -1.259   log_noise: -4.714\n",
-      "Iter 27/50 - Loss: 18.512   log_lengthscale: -1.295   log_noise: -4.714\n",
-      "Iter 28/50 - Loss: 11.126   log_lengthscale: -1.323   log_noise: -4.714\n",
-      "Iter 29/50 - Loss: 10.259   log_lengthscale: -1.329   log_noise: -4.714\n",
-      "Iter 30/50 - Loss: 5.683   log_lengthscale: -1.325   log_noise: -4.714\n",
-      "Iter 31/50 - Loss: 5.724   log_lengthscale: -1.315   log_noise: -4.714\n",
-      "Iter 32/50 - Loss: -2.059   log_lengthscale: -1.299   log_noise: -4.714\n",
-      "Iter 33/50 - Loss: -7.481   log_lengthscale: -1.283   log_noise: -4.714\n",
-      "Iter 34/50 - Loss: -7.562   log_lengthscale: -1.274   log_noise: -4.714\n",
-      "Iter 35/50 - Loss: -11.460   log_lengthscale: -1.281   log_noise: -4.714\n",
-      "Iter 36/50 - Loss: -16.088   log_lengthscale: -1.296   log_noise: -4.714\n",
-      "Iter 37/50 - Loss: -14.856   log_lengthscale: -1.317   log_noise: -4.714\n",
-      "Iter 38/50 - Loss: -24.291   log_lengthscale: -1.342   log_noise: -4.714\n",
-      "Iter 39/50 - Loss: -21.514   log_lengthscale: -1.365   log_noise: -4.714\n",
-      "Iter 40/50 - Loss: -27.827   log_lengthscale: -1.373   log_noise: -4.714\n",
-      "Iter 41/50 - Loss: -25.034   log_lengthscale: -1.378   log_noise: -4.714\n",
-      "Iter 42/50 - Loss: -24.770   log_lengthscale: -1.379   log_noise: -4.714\n",
-      "Iter 43/50 - Loss: -29.792   log_lengthscale: -1.386   log_noise: -4.714\n",
-      "Iter 44/50 - Loss: -29.995   log_lengthscale: -1.389   log_noise: -4.714\n",
-      "Iter 45/50 - Loss: -31.911   log_lengthscale: -1.385   log_noise: -4.714\n",
-      "Iter 46/50 - Loss: -36.235   log_lengthscale: -1.394   log_noise: -4.714\n",
-      "Iter 47/50 - Loss: -38.634   log_lengthscale: -1.406   log_noise: -4.714\n",
-      "Iter 48/50 - Loss: -38.393   log_lengthscale: -1.414   log_noise: -4.714\n",
-      "Iter 49/50 - Loss: -38.818   log_lengthscale: -1.436   log_noise: -4.714\n",
-      "Iter 50/50 - Loss: -40.494   log_lengthscale: -1.461   log_noise: -4.714\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Find optimal model hyperparameters\n",
-    "model.train()\n",
-    "likelihood.train()\n",
-    "\n",
-    "# Use the adam optimizer\n",
-    "optimizer = torch.optim.Adam([\n",
-    "    {'params': model.parameters()},  # Includes GaussianLikelihood parameters\n",
-    "], lr=0.1)\n",
-    "\n",
-    "# \"Loss\" for GPs - the marginal log likelihood\n",
-    "mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)\n",
-    "\n",
-    "n_iter = 50\n",
-    "for i in range(n_iter):\n",
-    "    optimizer.zero_grad()\n",
-    "    output = model(train_x)\n",
-    "    loss = -mll(output, train_y)\n",
-    "    loss.backward()\n",
-    "    print('Iter %d/%d - Loss: %.3f   log_lengthscale: %.3f   log_noise: %.3f' % (\n",
-    "        i + 1, n_iter, loss.item(),\n",
-    "        model.covar_module.base_kernel.log_lengthscale.item(),\n",
-    "        model.likelihood.log_noise.item()\n",
-    "    ))\n",
-    "    optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Predicting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x720 with 6 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set into eval mode\n",
-    "model.eval()\n",
-    "likelihood.eval()\n",
-    "\n",
-    "# Initialize plots\n",
-    "fig, ax = plt.subplots(2, 3, figsize=(14, 10))\n",
-    "\n",
-    "# Test points\n",
-    "n1, n2 = 50, 50\n",
-    "xv, yv = torch.meshgrid([torch.linspace(0, 1, n1), torch.linspace(0, 1, n2)])\n",
-    "f, dfx, dfy = franke(xv, yv)\n",
-    "\n",
-    "# Make predictions\n",
-    "with torch.no_grad(), gpytorch.settings.max_cg_iterations(50):\n",
-    "    test_x = torch.stack([xv.reshape(n1*n2, 1), yv.reshape(n1*n2, 1)], -1).squeeze(1)\n",
-    "    predictions = likelihood(model(test_x))\n",
-    "    mean = predictions.mean\n",
-    "\n",
-    "extent = (xv.min(), xv.max(), yv.max(), yv.min())\n",
-    "ax[0, 0].imshow(f, extent=extent, cmap=cm.jet)\n",
-    "ax[0, 0].set_title('True values')\n",
-    "ax[0, 1].imshow(dfx, extent=extent, cmap=cm.jet)\n",
-    "ax[0, 1].set_title('True x-derivatives')\n",
-    "ax[0, 2].imshow(dfy, extent=extent, cmap=cm.jet)\n",
-    "ax[0, 2].set_title('True y-derivatives')\n",
-    "\n",
-    "ax[1, 0].imshow(mean[:, 0].detach().numpy().reshape(n1, n2), extent=extent, cmap=cm.jet)\n",
-    "ax[1, 0].set_title('Predicted values')\n",
-    "ax[1, 1].imshow(mean[:, 1].detach().numpy().reshape(n1, n2), extent=extent, cmap=cm.jet)\n",
-    "ax[1, 1].set_title('Predicted x-derivatives')\n",
-    "ax[1, 2].imshow(mean[:, 2].detach().numpy().reshape(n1, n2), extent=extent, cmap=cm.jet)\n",
-    "ax[1, 2].set_title('Predicted y-derivatives')\n",
-    "\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/gpytorch/kernels/rbf_kernel_grad.py b/gpytorch/kernels/rbf_kernel_grad.py
index 7157b52d0..5cf2e0543 100644
--- a/gpytorch/kernels/rbf_kernel_grad.py
+++ b/gpytorch/kernels/rbf_kernel_grad.py
@@ -23,7 +23,7 @@ def __init__(
         if ard_num_dims is not None:
             raise RuntimeError('ARD is not supported with derivative observations yet!')
 
-        super().__init__(
+        super(RBFKernelGrad, self).__init__(
             ard_num_dims=None,
             batch_size=1,
             active_dims=None,
@@ -36,41 +36,42 @@ def __init__(
 
     def forward(self, x1, x2, diag=False, **params):
         if len(x1.size()) == 2:
+            b = 1
             n1, d = x1.size()
             n2, d = x2.size()
         else:
-            _, n1, d = x1.size()
+            b, n1, d = x1.size()
             _, n2, _ = x2.size()
 
+        K = torch.zeros(b, n1 * (d + 1), n2 * (d + 1))
+
         if not diag:
             ell = self.lengthscale
-            K = torch.zeros(*x1.shape[:-2], n1 * (d + 1), n2 * (d + 1))
             x1_ = x1 / ell
             x2_ = x2 / ell
 
+            outer = x1_.view([b, n1, 1, d]) - x2_.view([b, 1, n2, d])
+            outer = torch.transpose(outer, -1, -2).contiguous()
+
             # 1) Kernel block
             diff = self._covar_dist(x1_, x2_, square_dist=True, **params)
             K_11 = diff.div_(-2).exp_()
             K[..., :n1, :n2] = K_11
 
-            shape_list = [1] * len(K_11.shape[:-2])
-            outer = x1_.view(shape_list + [n1, 1, d]) - x2_.view(shape_list + [1, n2, d])
-            outer = torch.transpose(outer, -1, -2).contiguous()
-
             # 2) First gradient block
-            outer1 = outer.view(shape_list + [n1, n2*d])
-            K[..., :n1, n2:] = (outer1  / ell) * K_11.repeat(shape_list + [1, d])
+            outer1 = outer.view([b, n1, n2*d])
+            K[..., :n1, n2:] = (outer1  / ell) * K_11.repeat([1, 1, d])
 
             # 3) Second gradient block
-            outer2 = outer.transpose(-1, -3).contiguous().view(shape_list + [n2, n1*d])
+            outer2 = outer.transpose(-1, -3).contiguous().view([b, n2, n1*d])
             outer2 = outer2.transpose(-1, -2) 
-            K[..., n1:, :n2] = - (outer2  / ell) * K_11.repeat(shape_list + [d, 1])
+            K[..., n1:, :n2] = - (outer2  / ell) * K_11.repeat([1, d, 1])
 
             # 4) Hessian block
-            outer3 = outer1.repeat(shape_list + [d, 1]) * outer2.repeat(shape_list + [1, d])
+            outer3 = outer1.repeat([1, d, 1]) * outer2.repeat([1, 1, d])
             kp = KroneckerProductLazyTensor(torch.eye(d, d), torch.ones(n1, n2))
             fact1 = kp.evaluate() / ell.pow(2) - outer3 / ell.pow(2)
-            K[..., n1:, n2:] = fact1 * K_11.repeat(shape_list + [d, d]) 
+            K[..., n1:, n2:] = fact1 * K_11.repeat([1, d, d]) 
 
             # Symmetrize for stability
             if n1 == n2 and torch.eq(x1, x2).all():  
@@ -82,8 +83,11 @@ def forward(self, x1, x2, diag=False, **params):
             K = K[..., pi1, :][..., :, pi2]
 
             return K
-            
+
         else: # TODO: This will change when ARD is supported
+            if not (n1 == n2 and torch.eq(x1, x2).all()):
+                raise RuntimeError('diag=True only works when x1 == x2')
+
             kernel_diag = super(RBFKernelGrad, self).forward(x1, x2, diag=True)
             grad_diag = (1 / self.lengthscale.item() ** 2) * torch.ones(1, n2*d)
             k_diag = torch.cat((kernel_diag, grad_diag), dim=-1)
diff --git a/test/kernels/test_rbf_kernel_grad.py b/test/kernels/test_rbf_kernel_grad.py
new file mode 100644
index 000000000..6fbb52105
--- /dev/null
+++ b/test/kernels/test_rbf_kernel_grad.py
@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+
+import math
+import torch
+import unittest
+from gpytorch.kernels import RBFKernelGrad
+
+
+class TestRBFKernelGrad(unittest.TestCase):
+    def test_kernel(self):
+        a = torch.tensor([[[1, 2], [2, 4]]], dtype=torch.float)
+        b = torch.tensor([[[1, 3], [0, 4]]], dtype=torch.float)
+
+        kernel = RBFKernelGrad()
+        res = kernel(a, b).evaluate()
+        
+        actual = torch.tensor([
+            [0.35321, 0, -0.73517, 0.0054977, 0.011443, -0.022886],
+            [0, 0.73517, 0, -0.011443, -0.012374, 0.047633],
+            [0.73517, 0, -0.79499, 0.022886, 0.047633, -0.083824], 
+            [0.12476, 0.25967, 0.25967, 0.015565, 0.064793, 0],
+            [-0.25967, -0.2808, -0.54047, -0.064793, -0.23732, 0],
+            [-0.25967, -0.54047, -0.2808, 0, 0, 0.032396]])
+
+        self.assertLess(torch.norm(res - actual), 1e-5)
+
+    def test_kernel_batch(self):
+        a = torch.tensor([[[1, 2, 3], [2, 4, 0]], [[-1, 1, 2], [2, 1, 4]]], dtype=torch.float)
+        b = torch.tensor([[[1, 3, 1]], [[2, -1, 0]]], dtype=torch.float).repeat(1, 2, 1)
+
+        kernel = RBFKernelGrad()
+        res = kernel(a, b).evaluate()
+
+        # Compute each batch separately
+        actual = torch.zeros(2, 8, 8)        
+        actual[0, :, :] = kernel(a[0, :, :].squeeze(), b[0, :, :].squeeze()).evaluate()
+        actual[1, :, :] = kernel(a[1, :, :].squeeze(), b[1, :, :].squeeze()).evaluate()
+
+        self.assertLess(torch.norm(res - actual), 1e-5)
+
+    def test_initialize_lengthscale(self):
+        kernel = RBFKernelGrad()
+        kernel.initialize(lengthscale=3.14)
+        actual_value = torch.tensor(3.14).view_as(kernel.lengthscale)
+        self.assertLess(torch.norm(kernel.lengthscale - actual_value), 1e-5)
+
+    # def test_initialize_lengthscale_batch(self):
+    #     kernel = RBFKernelGrad(batch_size=2)
+    #     ls_init = torch.tensor([3.14, 4.13])
+    #     kernel.initialize(lengthscale=ls_init)
+    #     actual_value = ls_init.view_as(kernel.lengthscale)
+    #     self.assertLess(torch.norm(kernel.lengthscale - actual_value), 1e-5)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/means/test_constant_mean_grad.py b/test/means/test_constant_mean_grad.py
new file mode 100644
index 000000000..38fbb69e1
--- /dev/null
+++ b/test/means/test_constant_mean_grad.py
@@ -0,0 +1,25 @@
+#!/usr/bin/env python3
+
+import torch
+import unittest
+from gpytorch.means import ConstantMeanGrad
+
+
+class TestConstantMeanGrad(unittest.TestCase):
+    def setUp(self):
+        self.mean = ConstantMeanGrad()
+        self.mean.constant.data.fill_(5)
+
+    def test_forward(self):
+        a = torch.tensor([[1, 2], [2, 4]], dtype=torch.float)
+        res = self.mean(a)
+        self.assertEqual(tuple(res.size()), (2, 3))
+        self.assertTrue(res[:, 0].eq(5).all())
+        self.assertTrue(res[:, 1:].eq(0).all())
+
+    def test_forward_batch(self):
+        a = torch.tensor([[[1, 2], [1, 2], [2, 4]], [[2, 3], [2, 3], [1, 3]]], dtype=torch.float)
+        res = self.mean(a)
+        self.assertEqual(tuple(res.size()), (2, 3, 3))
+        self.assertTrue(res[:, :, 0].eq(5).all())
+        self.assertTrue(res[:, :, 1:].eq(0).all())

From 6153d68b869d646738f5d1f3da4b710c4ac909f3 Mon Sep 17 00:00:00 2001
From: dme65 <dme65@cornell.edu>
Date: Fri, 11 Jan 2019 12:28:37 -0500
Subject: [PATCH 09/25] Adding comments

---
 ...Regression_Derivative_Information_1d.ipynb | 102 +++++++++---------
 ...Regression_Derivative_Information_2d.ipynb | 102 +++++++++---------
 gpytorch/kernels/rbf_kernel_grad.py           |  15 +--
 3 files changed, 110 insertions(+), 109 deletions(-)

diff --git a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb
index 78b3f8d6e..f339969bc 100644
--- a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb
+++ b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb
@@ -112,56 +112,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iter 1/50 - Loss: 60.730   log_lengthscale: -0.367   log_noise: -4.265\n",
-      "Iter 2/50 - Loss: 59.618   log_lengthscale: -0.295   log_noise: -4.265\n",
-      "Iter 3/50 - Loss: 57.693   log_lengthscale: -0.225   log_noise: -4.265\n",
-      "Iter 4/50 - Loss: 55.330   log_lengthscale: -0.158   log_noise: -4.265\n",
-      "Iter 5/50 - Loss: 52.936   log_lengthscale: -0.096   log_noise: -4.265\n",
-      "Iter 6/50 - Loss: 52.683   log_lengthscale: -0.043   log_noise: -4.265\n",
-      "Iter 7/50 - Loss: 48.544   log_lengthscale: -0.005   log_noise: -4.265\n",
-      "Iter 8/50 - Loss: 49.339   log_lengthscale: 0.020   log_noise: -4.265\n",
-      "Iter 9/50 - Loss: 46.650   log_lengthscale: 0.035   log_noise: -4.265\n",
-      "Iter 10/50 - Loss: 46.521   log_lengthscale: 0.039   log_noise: -4.265\n",
-      "Iter 11/50 - Loss: 43.292   log_lengthscale: 0.038   log_noise: -4.265\n",
-      "Iter 12/50 - Loss: 40.677   log_lengthscale: 0.029   log_noise: -4.265\n",
-      "Iter 13/50 - Loss: 39.721   log_lengthscale: 0.019   log_noise: -4.265\n",
-      "Iter 14/50 - Loss: 37.023   log_lengthscale: 0.015   log_noise: -4.265\n",
-      "Iter 15/50 - Loss: 37.851   log_lengthscale: 0.018   log_noise: -4.265\n",
-      "Iter 16/50 - Loss: 31.790   log_lengthscale: 0.028   log_noise: -4.265\n",
-      "Iter 17/50 - Loss: 32.158   log_lengthscale: 0.037   log_noise: -4.265\n",
-      "Iter 18/50 - Loss: 31.662   log_lengthscale: 0.049   log_noise: -4.265\n",
-      "Iter 19/50 - Loss: 27.468   log_lengthscale: 0.065   log_noise: -4.265\n",
-      "Iter 20/50 - Loss: 23.470   log_lengthscale: 0.077   log_noise: -4.265\n",
-      "Iter 21/50 - Loss: 22.215   log_lengthscale: 0.086   log_noise: -4.265\n",
-      "Iter 22/50 - Loss: 21.394   log_lengthscale: 0.096   log_noise: -4.265\n",
-      "Iter 23/50 - Loss: 20.012   log_lengthscale: 0.104   log_noise: -4.265\n",
-      "Iter 24/50 - Loss: 17.207   log_lengthscale: 0.110   log_noise: -4.265\n",
-      "Iter 25/50 - Loss: 18.864   log_lengthscale: 0.115   log_noise: -4.265\n",
-      "Iter 26/50 - Loss: 12.822   log_lengthscale: 0.119   log_noise: -4.265\n",
-      "Iter 27/50 - Loss: 14.679   log_lengthscale: 0.123   log_noise: -4.265\n",
-      "Iter 28/50 - Loss: 12.624   log_lengthscale: 0.133   log_noise: -4.265\n",
-      "Iter 29/50 - Loss: 12.586   log_lengthscale: 0.146   log_noise: -4.265\n",
-      "Iter 30/50 - Loss: 9.183   log_lengthscale: 0.161   log_noise: -4.265\n",
-      "Iter 31/50 - Loss: 7.975   log_lengthscale: 0.176   log_noise: -4.265\n",
-      "Iter 32/50 - Loss: 10.570   log_lengthscale: 0.186   log_noise: -4.265\n",
-      "Iter 33/50 - Loss: 1.626   log_lengthscale: 0.194   log_noise: -4.265\n",
-      "Iter 34/50 - Loss: 3.866   log_lengthscale: 0.195   log_noise: -4.265\n",
-      "Iter 35/50 - Loss: 0.209   log_lengthscale: 0.189   log_noise: -4.265\n",
-      "Iter 36/50 - Loss: -0.803   log_lengthscale: 0.180   log_noise: -4.265\n",
-      "Iter 37/50 - Loss: 0.293   log_lengthscale: 0.173   log_noise: -4.265\n",
-      "Iter 38/50 - Loss: -3.192   log_lengthscale: 0.171   log_noise: -4.265\n",
-      "Iter 39/50 - Loss: -3.721   log_lengthscale: 0.178   log_noise: -4.265\n",
-      "Iter 40/50 - Loss: -6.172   log_lengthscale: 0.190   log_noise: -4.265\n",
-      "Iter 41/50 - Loss: -5.489   log_lengthscale: 0.203   log_noise: -4.265\n",
-      "Iter 42/50 - Loss: -6.737   log_lengthscale: 0.220   log_noise: -4.265\n",
-      "Iter 43/50 - Loss: -8.844   log_lengthscale: 0.225   log_noise: -4.265\n",
-      "Iter 44/50 - Loss: -6.363   log_lengthscale: 0.232   log_noise: -4.265\n",
-      "Iter 45/50 - Loss: -8.511   log_lengthscale: 0.235   log_noise: -4.265\n",
-      "Iter 46/50 - Loss: -9.528   log_lengthscale: 0.238   log_noise: -4.265\n",
-      "Iter 47/50 - Loss: -8.002   log_lengthscale: 0.237   log_noise: -4.265\n",
-      "Iter 48/50 - Loss: -13.198   log_lengthscale: 0.244   log_noise: -4.265\n",
-      "Iter 49/50 - Loss: -15.427   log_lengthscale: 0.244   log_noise: -4.265\n",
-      "Iter 50/50 - Loss: -11.407   log_lengthscale: 0.235   log_noise: -4.265\n"
+      "Iter 1/50 - Loss: 60.670   log_lengthscale: -0.367   log_noise: -4.270\n",
+      "Iter 2/50 - Loss: 57.422   log_lengthscale: -0.295   log_noise: -4.270\n",
+      "Iter 3/50 - Loss: 58.583   log_lengthscale: -0.226   log_noise: -4.270\n",
+      "Iter 4/50 - Loss: 55.612   log_lengthscale: -0.159   log_noise: -4.270\n",
+      "Iter 5/50 - Loss: 53.394   log_lengthscale: -0.098   log_noise: -4.270\n",
+      "Iter 6/50 - Loss: 53.093   log_lengthscale: -0.044   log_noise: -4.270\n",
+      "Iter 7/50 - Loss: 49.333   log_lengthscale: 0.000   log_noise: -4.270\n",
+      "Iter 8/50 - Loss: 47.567   log_lengthscale: 0.034   log_noise: -4.270\n",
+      "Iter 9/50 - Loss: 44.151   log_lengthscale: 0.049   log_noise: -4.270\n",
+      "Iter 10/50 - Loss: 43.650   log_lengthscale: 0.047   log_noise: -4.270\n",
+      "Iter 11/50 - Loss: 42.709   log_lengthscale: 0.041   log_noise: -4.270\n",
+      "Iter 12/50 - Loss: 40.265   log_lengthscale: 0.029   log_noise: -4.270\n",
+      "Iter 13/50 - Loss: 38.733   log_lengthscale: 0.016   log_noise: -4.270\n",
+      "Iter 14/50 - Loss: 37.748   log_lengthscale: 0.004   log_noise: -4.270\n",
+      "Iter 15/50 - Loss: 36.179   log_lengthscale: 0.001   log_noise: -4.270\n",
+      "Iter 16/50 - Loss: 32.374   log_lengthscale: 0.004   log_noise: -4.270\n",
+      "Iter 17/50 - Loss: 30.942   log_lengthscale: 0.011   log_noise: -4.270\n",
+      "Iter 18/50 - Loss: 31.481   log_lengthscale: 0.024   log_noise: -4.270\n",
+      "Iter 19/50 - Loss: 27.437   log_lengthscale: 0.048   log_noise: -4.270\n",
+      "Iter 20/50 - Loss: 23.913   log_lengthscale: 0.076   log_noise: -4.270\n",
+      "Iter 21/50 - Loss: 23.246   log_lengthscale: 0.102   log_noise: -4.270\n",
+      "Iter 22/50 - Loss: 20.868   log_lengthscale: 0.124   log_noise: -4.270\n",
+      "Iter 23/50 - Loss: 19.050   log_lengthscale: 0.141   log_noise: -4.270\n",
+      "Iter 24/50 - Loss: 17.217   log_lengthscale: 0.152   log_noise: -4.270\n",
+      "Iter 25/50 - Loss: 13.340   log_lengthscale: 0.153   log_noise: -4.270\n",
+      "Iter 26/50 - Loss: 12.532   log_lengthscale: 0.145   log_noise: -4.270\n",
+      "Iter 27/50 - Loss: 11.423   log_lengthscale: 0.134   log_noise: -4.270\n",
+      "Iter 28/50 - Loss: 13.224   log_lengthscale: 0.125   log_noise: -4.270\n",
+      "Iter 29/50 - Loss: 8.808   log_lengthscale: 0.116   log_noise: -4.270\n",
+      "Iter 30/50 - Loss: 5.781   log_lengthscale: 0.113   log_noise: -4.270\n",
+      "Iter 31/50 - Loss: 6.906   log_lengthscale: 0.111   log_noise: -4.270\n",
+      "Iter 32/50 - Loss: 4.585   log_lengthscale: 0.119   log_noise: -4.270\n",
+      "Iter 33/50 - Loss: 1.655   log_lengthscale: 0.132   log_noise: -4.270\n",
+      "Iter 34/50 - Loss: 5.168   log_lengthscale: 0.147   log_noise: -4.270\n",
+      "Iter 35/50 - Loss: 1.142   log_lengthscale: 0.167   log_noise: -4.270\n",
+      "Iter 36/50 - Loss: -3.013   log_lengthscale: 0.190   log_noise: -4.270\n",
+      "Iter 37/50 - Loss: -1.479   log_lengthscale: 0.205   log_noise: -4.270\n",
+      "Iter 38/50 - Loss: -1.081   log_lengthscale: 0.215   log_noise: -4.270\n",
+      "Iter 39/50 - Loss: -0.491   log_lengthscale: 0.221   log_noise: -4.270\n",
+      "Iter 40/50 - Loss: -3.668   log_lengthscale: 0.228   log_noise: -4.270\n",
+      "Iter 41/50 - Loss: -6.196   log_lengthscale: 0.228   log_noise: -4.270\n",
+      "Iter 42/50 - Loss: -6.167   log_lengthscale: 0.221   log_noise: -4.270\n",
+      "Iter 43/50 - Loss: -9.753   log_lengthscale: 0.211   log_noise: -4.270\n",
+      "Iter 44/50 - Loss: -5.198   log_lengthscale: 0.200   log_noise: -4.270\n",
+      "Iter 45/50 - Loss: -11.275   log_lengthscale: 0.197   log_noise: -4.270\n",
+      "Iter 46/50 - Loss: -11.197   log_lengthscale: 0.191   log_noise: -4.270\n",
+      "Iter 47/50 - Loss: -9.503   log_lengthscale: 0.186   log_noise: -4.270\n",
+      "Iter 48/50 - Loss: -11.020   log_lengthscale: 0.188   log_noise: -4.270\n",
+      "Iter 49/50 - Loss: -8.434   log_lengthscale: 0.192   log_noise: -4.270\n",
+      "Iter 50/50 - Loss: -12.362   log_lengthscale: 0.203   log_noise: -4.270\n"
      ]
     }
    ],
@@ -207,7 +207,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 2 Axes>"
       ]
diff --git a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb
index 421dfced1..d4e7c630b 100644
--- a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb
+++ b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb
@@ -136,56 +136,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iter 1/50 - Loss: 110.809   log_lengthscale: -0.367   log_noise: -4.712\n",
-      "Iter 2/50 - Loss: 107.447   log_lengthscale: -0.439   log_noise: -4.712\n",
-      "Iter 3/50 - Loss: 104.017   log_lengthscale: -0.514   log_noise: -4.712\n",
-      "Iter 4/50 - Loss: 100.469   log_lengthscale: -0.590   log_noise: -4.712\n",
-      "Iter 5/50 - Loss: 95.480   log_lengthscale: -0.668   log_noise: -4.712\n",
-      "Iter 6/50 - Loss: 94.004   log_lengthscale: -0.748   log_noise: -4.712\n",
-      "Iter 7/50 - Loss: 86.962   log_lengthscale: -0.829   log_noise: -4.712\n",
-      "Iter 8/50 - Loss: 80.542   log_lengthscale: -0.912   log_noise: -4.712\n",
-      "Iter 9/50 - Loss: 81.405   log_lengthscale: -0.997   log_noise: -4.712\n",
-      "Iter 10/50 - Loss: 73.289   log_lengthscale: -1.082   log_noise: -4.712\n",
-      "Iter 11/50 - Loss: 74.072   log_lengthscale: -1.165   log_noise: -4.712\n",
-      "Iter 12/50 - Loss: 73.329   log_lengthscale: -1.232   log_noise: -4.712\n",
-      "Iter 13/50 - Loss: 68.476   log_lengthscale: -1.275   log_noise: -4.712\n",
-      "Iter 14/50 - Loss: 63.706   log_lengthscale: -1.296   log_noise: -4.712\n",
-      "Iter 15/50 - Loss: 63.301   log_lengthscale: -1.291   log_noise: -4.712\n",
-      "Iter 16/50 - Loss: 57.303   log_lengthscale: -1.266   log_noise: -4.712\n",
-      "Iter 17/50 - Loss: 54.499   log_lengthscale: -1.231   log_noise: -4.712\n",
-      "Iter 18/50 - Loss: 49.428   log_lengthscale: -1.193   log_noise: -4.712\n",
-      "Iter 19/50 - Loss: 41.405   log_lengthscale: -1.153   log_noise: -4.712\n",
-      "Iter 20/50 - Loss: 36.237   log_lengthscale: -1.130   log_noise: -4.712\n",
-      "Iter 21/50 - Loss: 35.812   log_lengthscale: -1.127   log_noise: -4.712\n",
-      "Iter 22/50 - Loss: 32.704   log_lengthscale: -1.142   log_noise: -4.712\n",
-      "Iter 23/50 - Loss: 29.561   log_lengthscale: -1.168   log_noise: -4.712\n",
-      "Iter 24/50 - Loss: 29.646   log_lengthscale: -1.205   log_noise: -4.712\n",
-      "Iter 25/50 - Loss: 22.450   log_lengthscale: -1.247   log_noise: -4.712\n",
-      "Iter 26/50 - Loss: 23.551   log_lengthscale: -1.292   log_noise: -4.712\n",
-      "Iter 27/50 - Loss: 16.627   log_lengthscale: -1.323   log_noise: -4.712\n",
-      "Iter 28/50 - Loss: 11.997   log_lengthscale: -1.340   log_noise: -4.712\n",
-      "Iter 29/50 - Loss: 14.768   log_lengthscale: -1.351   log_noise: -4.712\n",
-      "Iter 30/50 - Loss: 10.343   log_lengthscale: -1.344   log_noise: -4.712\n",
-      "Iter 31/50 - Loss: 6.235   log_lengthscale: -1.329   log_noise: -4.712\n",
-      "Iter 32/50 - Loss: 1.952   log_lengthscale: -1.307   log_noise: -4.712\n",
-      "Iter 33/50 - Loss: -0.172   log_lengthscale: -1.285   log_noise: -4.712\n",
-      "Iter 34/50 - Loss: -5.733   log_lengthscale: -1.267   log_noise: -4.712\n",
-      "Iter 35/50 - Loss: -0.967   log_lengthscale: -1.268   log_noise: -4.712\n",
-      "Iter 36/50 - Loss: -12.498   log_lengthscale: -1.286   log_noise: -4.712\n",
-      "Iter 37/50 - Loss: -14.981   log_lengthscale: -1.319   log_noise: -4.712\n",
-      "Iter 38/50 - Loss: -16.068   log_lengthscale: -1.358   log_noise: -4.712\n",
-      "Iter 39/50 - Loss: -20.499   log_lengthscale: -1.396   log_noise: -4.712\n",
-      "Iter 40/50 - Loss: -11.844   log_lengthscale: -1.426   log_noise: -4.712\n",
-      "Iter 41/50 - Loss: -19.826   log_lengthscale: -1.444   log_noise: -4.712\n",
-      "Iter 42/50 - Loss: -15.652   log_lengthscale: -1.448   log_noise: -4.712\n",
-      "Iter 43/50 - Loss: -26.009   log_lengthscale: -1.435   log_noise: -4.712\n",
-      "Iter 44/50 - Loss: -31.040   log_lengthscale: -1.422   log_noise: -4.712\n",
-      "Iter 45/50 - Loss: -25.432   log_lengthscale: -1.408   log_noise: -4.712\n",
-      "Iter 46/50 - Loss: -34.473   log_lengthscale: -1.398   log_noise: -4.712\n",
-      "Iter 47/50 - Loss: -35.418   log_lengthscale: -1.403   log_noise: -4.712\n",
-      "Iter 48/50 - Loss: -34.949   log_lengthscale: -1.425   log_noise: -4.712\n",
-      "Iter 49/50 - Loss: -35.762   log_lengthscale: -1.455   log_noise: -4.712\n",
-      "Iter 50/50 - Loss: -36.127   log_lengthscale: -1.487   log_noise: -4.712\n"
+      "Iter 1/50 - Loss: 110.857   log_lengthscale: -0.367   log_noise: -4.709\n",
+      "Iter 2/50 - Loss: 107.436   log_lengthscale: -0.439   log_noise: -4.709\n",
+      "Iter 3/50 - Loss: 103.931   log_lengthscale: -0.514   log_noise: -4.709\n",
+      "Iter 4/50 - Loss: 98.777   log_lengthscale: -0.590   log_noise: -4.709\n",
+      "Iter 5/50 - Loss: 96.182   log_lengthscale: -0.668   log_noise: -4.709\n",
+      "Iter 6/50 - Loss: 90.462   log_lengthscale: -0.748   log_noise: -4.709\n",
+      "Iter 7/50 - Loss: 87.768   log_lengthscale: -0.829   log_noise: -4.709\n",
+      "Iter 8/50 - Loss: 81.940   log_lengthscale: -0.912   log_noise: -4.709\n",
+      "Iter 9/50 - Loss: 77.007   log_lengthscale: -0.996   log_noise: -4.709\n",
+      "Iter 10/50 - Loss: 71.270   log_lengthscale: -1.081   log_noise: -4.709\n",
+      "Iter 11/50 - Loss: 73.652   log_lengthscale: -1.162   log_noise: -4.709\n",
+      "Iter 12/50 - Loss: 71.459   log_lengthscale: -1.224   log_noise: -4.709\n",
+      "Iter 13/50 - Loss: 66.044   log_lengthscale: -1.263   log_noise: -4.709\n",
+      "Iter 14/50 - Loss: 65.438   log_lengthscale: -1.284   log_noise: -4.709\n",
+      "Iter 15/50 - Loss: 66.275   log_lengthscale: -1.282   log_noise: -4.709\n",
+      "Iter 16/50 - Loss: 54.533   log_lengthscale: -1.266   log_noise: -4.709\n",
+      "Iter 17/50 - Loss: 50.374   log_lengthscale: -1.240   log_noise: -4.709\n",
+      "Iter 18/50 - Loss: 48.896   log_lengthscale: -1.215   log_noise: -4.709\n",
+      "Iter 19/50 - Loss: 39.250   log_lengthscale: -1.186   log_noise: -4.709\n",
+      "Iter 20/50 - Loss: 40.558   log_lengthscale: -1.171   log_noise: -4.709\n",
+      "Iter 21/50 - Loss: 36.031   log_lengthscale: -1.169   log_noise: -4.709\n",
+      "Iter 22/50 - Loss: 30.555   log_lengthscale: -1.176   log_noise: -4.709\n",
+      "Iter 23/50 - Loss: 28.628   log_lengthscale: -1.192   log_noise: -4.709\n",
+      "Iter 24/50 - Loss: 25.020   log_lengthscale: -1.219   log_noise: -4.709\n",
+      "Iter 25/50 - Loss: 23.261   log_lengthscale: -1.253   log_noise: -4.709\n",
+      "Iter 26/50 - Loss: 16.194   log_lengthscale: -1.286   log_noise: -4.709\n",
+      "Iter 27/50 - Loss: 17.212   log_lengthscale: -1.304   log_noise: -4.709\n",
+      "Iter 28/50 - Loss: 10.664   log_lengthscale: -1.316   log_noise: -4.709\n",
+      "Iter 29/50 - Loss: 11.396   log_lengthscale: -1.322   log_noise: -4.709\n",
+      "Iter 30/50 - Loss: 5.026   log_lengthscale: -1.316   log_noise: -4.709\n",
+      "Iter 31/50 - Loss: 2.685   log_lengthscale: -1.304   log_noise: -4.709\n",
+      "Iter 32/50 - Loss: -0.279   log_lengthscale: -1.293   log_noise: -4.709\n",
+      "Iter 33/50 - Loss: -2.255   log_lengthscale: -1.292   log_noise: -4.709\n",
+      "Iter 34/50 - Loss: -6.855   log_lengthscale: -1.305   log_noise: -4.709\n",
+      "Iter 35/50 - Loss: -11.115   log_lengthscale: -1.329   log_noise: -4.709\n",
+      "Iter 36/50 - Loss: -13.209   log_lengthscale: -1.359   log_noise: -4.709\n",
+      "Iter 37/50 - Loss: -17.973   log_lengthscale: -1.390   log_noise: -4.709\n",
+      "Iter 38/50 - Loss: -18.122   log_lengthscale: -1.423   log_noise: -4.709\n",
+      "Iter 39/50 - Loss: -18.402   log_lengthscale: -1.441   log_noise: -4.709\n",
+      "Iter 40/50 - Loss: -20.202   log_lengthscale: -1.439   log_noise: -4.709\n",
+      "Iter 41/50 - Loss: -22.876   log_lengthscale: -1.421   log_noise: -4.709\n",
+      "Iter 42/50 - Loss: -24.881   log_lengthscale: -1.409   log_noise: -4.709\n",
+      "Iter 43/50 - Loss: -27.347   log_lengthscale: -1.402   log_noise: -4.709\n",
+      "Iter 44/50 - Loss: -31.765   log_lengthscale: -1.408   log_noise: -4.709\n",
+      "Iter 45/50 - Loss: -29.013   log_lengthscale: -1.429   log_noise: -4.709\n",
+      "Iter 46/50 - Loss: -34.779   log_lengthscale: -1.460   log_noise: -4.709\n",
+      "Iter 47/50 - Loss: -35.382   log_lengthscale: -1.489   log_noise: -4.709\n",
+      "Iter 48/50 - Loss: -34.789   log_lengthscale: -1.513   log_noise: -4.709\n",
+      "Iter 49/50 - Loss: -33.586   log_lengthscale: -1.537   log_noise: -4.709\n",
+      "Iter 50/50 - Loss: -36.207   log_lengthscale: -1.557   log_noise: -4.709\n"
      ]
     }
    ],
@@ -231,7 +231,7 @@
    "outputs": [
     {
      "data": {
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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1008x720 with 6 Axes>"
       ]
diff --git a/gpytorch/kernels/rbf_kernel_grad.py b/gpytorch/kernels/rbf_kernel_grad.py
index 5cf2e0543..58312aaf4 100644
--- a/gpytorch/kernels/rbf_kernel_grad.py
+++ b/gpytorch/kernels/rbf_kernel_grad.py
@@ -35,22 +35,23 @@ def __init__(
         )
 
     def forward(self, x1, x2, diag=False, **params):
+        b = 1
         if len(x1.size()) == 2:
-            b = 1
             n1, d = x1.size()
-            n2, d = x2.size()
+            n2, _ = x2.size()
         else:
             b, n1, d = x1.size()
             _, n2, _ = x2.size()
 
-        K = torch.zeros(b, n1 * (d + 1), n2 * (d + 1))
+        K = torch.zeros(b, n1 * (d + 1), n2 * (d + 1))  # batch x n1(d+1) x n2(d+1)
 
         if not diag:
             ell = self.lengthscale
             x1_ = x1 / ell
             x2_ = x2 / ell
 
-            outer = x1_.view([b, n1, 1, d]) - x2_.view([b, 1, n2, d])
+            # Form all possible rank-1 product for the gradient and Hessian blocks
+            outer = x1_.view([b, n1, 1, d]) - x2_.view([b, 1, n2, d])  
             outer = torch.transpose(outer, -1, -2).contiguous()
 
             # 1) Kernel block
@@ -70,8 +71,8 @@ def forward(self, x1, x2, diag=False, **params):
             # 4) Hessian block
             outer3 = outer1.repeat([1, d, 1]) * outer2.repeat([1, 1, d])
             kp = KroneckerProductLazyTensor(torch.eye(d, d), torch.ones(n1, n2))
-            fact1 = kp.evaluate() / ell.pow(2) - outer3 / ell.pow(2)
-            K[..., n1:, n2:] = fact1 * K_11.repeat([1, d, d]) 
+            chain_rule = kp.evaluate() / ell.pow(2) - outer3 / ell.pow(2)
+            K[..., n1:, n2:] = chain_rule * K_11.repeat([1, d, d]) 
 
             # Symmetrize for stability
             if n1 == n2 and torch.eq(x1, x2).all():  
@@ -89,7 +90,7 @@ def forward(self, x1, x2, diag=False, **params):
                 raise RuntimeError('diag=True only works when x1 == x2')
 
             kernel_diag = super(RBFKernelGrad, self).forward(x1, x2, diag=True)
-            grad_diag = (1 / self.lengthscale.item() ** 2) * torch.ones(1, n2*d)
+            grad_diag = (1 / self.lengthscale.pow(2)) * torch.ones(1, n2*d)
             k_diag = torch.cat((kernel_diag, grad_diag), dim=-1)
             pi = torch.arange(n2 * (d + 1)).view(d + 1, n2).t().contiguous().view((n2 * (d + 1)))
             return k_diag[..., pi]

From d6bd72383dea31b613f621f97c0604b87cf0d363 Mon Sep 17 00:00:00 2001
From: dme65 <dme65@cornell.edu>
Date: Fri, 11 Jan 2019 14:23:39 -0500
Subject: [PATCH 10/25] Docs + linting

---
 docs/source/kernels.rst                       |   6 ++
 docs/source/means.rst                         |   6 ++
 .../README.md                                 |   3 +
 ...Regression_Derivative_Information_1d.ipynb | 102 +++++++++---------
 ...Regression_Derivative_Information_2d.ipynb | 102 +++++++++---------
 .../index.rst                                 |   9 ++
 gpytorch/kernels/rbf_kernel_grad.py           | 102 ++++++++++++------
 gpytorch/kernels/scale_kernel.py              |   2 +-
 gpytorch/means/constant_mean_grad.py          |   1 -
 test/kernels/test_rbf_kernel_grad.py          |  34 +++---
 10 files changed, 217 insertions(+), 150 deletions(-)
 create mode 100644 examples/10_GP_Regression_Derivative_Information/README.md
 create mode 100644 examples/10_GP_Regression_Derivative_Information/index.rst

diff --git a/docs/source/kernels.rst b/docs/source/kernels.rst
index 5ac842d3c..f9acc0c07 100644
--- a/docs/source/kernels.rst
+++ b/docs/source/kernels.rst
@@ -127,6 +127,12 @@ Specialty Kernels
 .. autoclass:: MultitaskKernel
    :members:
 
+:hidden:`RBFKernelGrad`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: RBFKernelGrad
+   :members:
+
 
 Kernels for Scalable GP Regression Methods
 --------------------------------------------
diff --git a/docs/source/means.rst b/docs/source/means.rst
index 4dcde4c56..324668988 100644
--- a/docs/source/means.rst
+++ b/docs/source/means.rst
@@ -39,3 +39,9 @@ Specialty Means
 
 .. autoclass:: MultitaskMean
    :members:
+
+:hidden:`ConstantMeanGrad`
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: ConstantMeanGrad
+   :members:
diff --git a/examples/10_GP_Regression_Derivative_Information/README.md b/examples/10_GP_Regression_Derivative_Information/README.md
new file mode 100644
index 000000000..b0b29f89c
--- /dev/null
+++ b/examples/10_GP_Regression_Derivative_Information/README.md
@@ -0,0 +1,3 @@
+# GP regression with derivative information
+
+This is a new feature, and is likely unstable. Enjoy!
diff --git a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb
index f339969bc..d2af1f216 100644
--- a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb
+++ b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb
@@ -112,56 +112,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iter 1/50 - Loss: 60.670   log_lengthscale: -0.367   log_noise: -4.270\n",
-      "Iter 2/50 - Loss: 57.422   log_lengthscale: -0.295   log_noise: -4.270\n",
-      "Iter 3/50 - Loss: 58.583   log_lengthscale: -0.226   log_noise: -4.270\n",
-      "Iter 4/50 - Loss: 55.612   log_lengthscale: -0.159   log_noise: -4.270\n",
-      "Iter 5/50 - Loss: 53.394   log_lengthscale: -0.098   log_noise: -4.270\n",
-      "Iter 6/50 - Loss: 53.093   log_lengthscale: -0.044   log_noise: -4.270\n",
-      "Iter 7/50 - Loss: 49.333   log_lengthscale: 0.000   log_noise: -4.270\n",
-      "Iter 8/50 - Loss: 47.567   log_lengthscale: 0.034   log_noise: -4.270\n",
-      "Iter 9/50 - Loss: 44.151   log_lengthscale: 0.049   log_noise: -4.270\n",
-      "Iter 10/50 - Loss: 43.650   log_lengthscale: 0.047   log_noise: -4.270\n",
-      "Iter 11/50 - Loss: 42.709   log_lengthscale: 0.041   log_noise: -4.270\n",
-      "Iter 12/50 - Loss: 40.265   log_lengthscale: 0.029   log_noise: -4.270\n",
-      "Iter 13/50 - Loss: 38.733   log_lengthscale: 0.016   log_noise: -4.270\n",
-      "Iter 14/50 - Loss: 37.748   log_lengthscale: 0.004   log_noise: -4.270\n",
-      "Iter 15/50 - Loss: 36.179   log_lengthscale: 0.001   log_noise: -4.270\n",
-      "Iter 16/50 - Loss: 32.374   log_lengthscale: 0.004   log_noise: -4.270\n",
-      "Iter 17/50 - Loss: 30.942   log_lengthscale: 0.011   log_noise: -4.270\n",
-      "Iter 18/50 - Loss: 31.481   log_lengthscale: 0.024   log_noise: -4.270\n",
-      "Iter 19/50 - Loss: 27.437   log_lengthscale: 0.048   log_noise: -4.270\n",
-      "Iter 20/50 - Loss: 23.913   log_lengthscale: 0.076   log_noise: -4.270\n",
-      "Iter 21/50 - Loss: 23.246   log_lengthscale: 0.102   log_noise: -4.270\n",
-      "Iter 22/50 - Loss: 20.868   log_lengthscale: 0.124   log_noise: -4.270\n",
-      "Iter 23/50 - Loss: 19.050   log_lengthscale: 0.141   log_noise: -4.270\n",
-      "Iter 24/50 - Loss: 17.217   log_lengthscale: 0.152   log_noise: -4.270\n",
-      "Iter 25/50 - Loss: 13.340   log_lengthscale: 0.153   log_noise: -4.270\n",
-      "Iter 26/50 - Loss: 12.532   log_lengthscale: 0.145   log_noise: -4.270\n",
-      "Iter 27/50 - Loss: 11.423   log_lengthscale: 0.134   log_noise: -4.270\n",
-      "Iter 28/50 - Loss: 13.224   log_lengthscale: 0.125   log_noise: -4.270\n",
-      "Iter 29/50 - Loss: 8.808   log_lengthscale: 0.116   log_noise: -4.270\n",
-      "Iter 30/50 - Loss: 5.781   log_lengthscale: 0.113   log_noise: -4.270\n",
-      "Iter 31/50 - Loss: 6.906   log_lengthscale: 0.111   log_noise: -4.270\n",
-      "Iter 32/50 - Loss: 4.585   log_lengthscale: 0.119   log_noise: -4.270\n",
-      "Iter 33/50 - Loss: 1.655   log_lengthscale: 0.132   log_noise: -4.270\n",
-      "Iter 34/50 - Loss: 5.168   log_lengthscale: 0.147   log_noise: -4.270\n",
-      "Iter 35/50 - Loss: 1.142   log_lengthscale: 0.167   log_noise: -4.270\n",
-      "Iter 36/50 - Loss: -3.013   log_lengthscale: 0.190   log_noise: -4.270\n",
-      "Iter 37/50 - Loss: -1.479   log_lengthscale: 0.205   log_noise: -4.270\n",
-      "Iter 38/50 - Loss: -1.081   log_lengthscale: 0.215   log_noise: -4.270\n",
-      "Iter 39/50 - Loss: -0.491   log_lengthscale: 0.221   log_noise: -4.270\n",
-      "Iter 40/50 - Loss: -3.668   log_lengthscale: 0.228   log_noise: -4.270\n",
-      "Iter 41/50 - Loss: -6.196   log_lengthscale: 0.228   log_noise: -4.270\n",
-      "Iter 42/50 - Loss: -6.167   log_lengthscale: 0.221   log_noise: -4.270\n",
-      "Iter 43/50 - Loss: -9.753   log_lengthscale: 0.211   log_noise: -4.270\n",
-      "Iter 44/50 - Loss: -5.198   log_lengthscale: 0.200   log_noise: -4.270\n",
-      "Iter 45/50 - Loss: -11.275   log_lengthscale: 0.197   log_noise: -4.270\n",
-      "Iter 46/50 - Loss: -11.197   log_lengthscale: 0.191   log_noise: -4.270\n",
-      "Iter 47/50 - Loss: -9.503   log_lengthscale: 0.186   log_noise: -4.270\n",
-      "Iter 48/50 - Loss: -11.020   log_lengthscale: 0.188   log_noise: -4.270\n",
-      "Iter 49/50 - Loss: -8.434   log_lengthscale: 0.192   log_noise: -4.270\n",
-      "Iter 50/50 - Loss: -12.362   log_lengthscale: 0.203   log_noise: -4.270\n"
+      "Iter 1/50 - Loss: 61.582   log_lengthscale: -0.367   log_noise: -4.269\n",
+      "Iter 2/50 - Loss: 58.703   log_lengthscale: -0.295   log_noise: -4.269\n",
+      "Iter 3/50 - Loss: 56.608   log_lengthscale: -0.226   log_noise: -4.269\n",
+      "Iter 4/50 - Loss: 54.290   log_lengthscale: -0.160   log_noise: -4.269\n",
+      "Iter 5/50 - Loss: 52.452   log_lengthscale: -0.100   log_noise: -4.269\n",
+      "Iter 6/50 - Loss: 51.232   log_lengthscale: -0.046   log_noise: -4.269\n",
+      "Iter 7/50 - Loss: 48.236   log_lengthscale: -0.006   log_noise: -4.269\n",
+      "Iter 8/50 - Loss: 47.637   log_lengthscale: 0.017   log_noise: -4.269\n",
+      "Iter 9/50 - Loss: 45.793   log_lengthscale: 0.029   log_noise: -4.269\n",
+      "Iter 10/50 - Loss: 46.485   log_lengthscale: 0.031   log_noise: -4.269\n",
+      "Iter 11/50 - Loss: 42.307   log_lengthscale: 0.029   log_noise: -4.269\n",
+      "Iter 12/50 - Loss: 40.820   log_lengthscale: 0.023   log_noise: -4.269\n",
+      "Iter 13/50 - Loss: 39.255   log_lengthscale: 0.014   log_noise: -4.269\n",
+      "Iter 14/50 - Loss: 36.660   log_lengthscale: 0.011   log_noise: -4.269\n",
+      "Iter 15/50 - Loss: 34.557   log_lengthscale: 0.012   log_noise: -4.269\n",
+      "Iter 16/50 - Loss: 33.867   log_lengthscale: 0.014   log_noise: -4.269\n",
+      "Iter 17/50 - Loss: 31.727   log_lengthscale: 0.016   log_noise: -4.269\n",
+      "Iter 18/50 - Loss: 27.939   log_lengthscale: 0.027   log_noise: -4.269\n",
+      "Iter 19/50 - Loss: 27.122   log_lengthscale: 0.042   log_noise: -4.269\n",
+      "Iter 20/50 - Loss: 24.861   log_lengthscale: 0.063   log_noise: -4.269\n",
+      "Iter 21/50 - Loss: 23.606   log_lengthscale: 0.083   log_noise: -4.269\n",
+      "Iter 22/50 - Loss: 21.119   log_lengthscale: 0.103   log_noise: -4.269\n",
+      "Iter 23/50 - Loss: 19.037   log_lengthscale: 0.114   log_noise: -4.269\n",
+      "Iter 24/50 - Loss: 16.896   log_lengthscale: 0.121   log_noise: -4.269\n",
+      "Iter 25/50 - Loss: 20.047   log_lengthscale: 0.122   log_noise: -4.269\n",
+      "Iter 26/50 - Loss: 15.217   log_lengthscale: 0.127   log_noise: -4.269\n",
+      "Iter 27/50 - Loss: 14.856   log_lengthscale: 0.133   log_noise: -4.269\n",
+      "Iter 28/50 - Loss: 11.275   log_lengthscale: 0.141   log_noise: -4.269\n",
+      "Iter 29/50 - Loss: 12.726   log_lengthscale: 0.152   log_noise: -4.269\n",
+      "Iter 30/50 - Loss: 5.189   log_lengthscale: 0.160   log_noise: -4.269\n",
+      "Iter 31/50 - Loss: 6.020   log_lengthscale: 0.161   log_noise: -4.269\n",
+      "Iter 32/50 - Loss: 4.733   log_lengthscale: 0.159   log_noise: -4.269\n",
+      "Iter 33/50 - Loss: 4.649   log_lengthscale: 0.158   log_noise: -4.269\n",
+      "Iter 34/50 - Loss: -0.154   log_lengthscale: 0.165   log_noise: -4.269\n",
+      "Iter 35/50 - Loss: 2.789   log_lengthscale: 0.173   log_noise: -4.269\n",
+      "Iter 36/50 - Loss: -0.807   log_lengthscale: 0.187   log_noise: -4.269\n",
+      "Iter 37/50 - Loss: 1.525   log_lengthscale: 0.207   log_noise: -4.269\n",
+      "Iter 38/50 - Loss: -2.236   log_lengthscale: 0.221   log_noise: -4.269\n",
+      "Iter 39/50 - Loss: -0.497   log_lengthscale: 0.227   log_noise: -4.269\n",
+      "Iter 40/50 - Loss: -4.761   log_lengthscale: 0.225   log_noise: -4.269\n",
+      "Iter 41/50 - Loss: -7.380   log_lengthscale: 0.214   log_noise: -4.269\n",
+      "Iter 42/50 - Loss: -5.682   log_lengthscale: 0.199   log_noise: -4.269\n",
+      "Iter 43/50 - Loss: -8.120   log_lengthscale: 0.188   log_noise: -4.269\n",
+      "Iter 44/50 - Loss: -7.820   log_lengthscale: 0.174   log_noise: -4.269\n",
+      "Iter 45/50 - Loss: -7.840   log_lengthscale: 0.168   log_noise: -4.269\n",
+      "Iter 46/50 - Loss: -9.724   log_lengthscale: 0.170   log_noise: -4.269\n",
+      "Iter 47/50 - Loss: -7.647   log_lengthscale: 0.183   log_noise: -4.269\n",
+      "Iter 48/50 - Loss: -10.614   log_lengthscale: 0.201   log_noise: -4.269\n",
+      "Iter 49/50 - Loss: -13.871   log_lengthscale: 0.220   log_noise: -4.269\n",
+      "Iter 50/50 - Loss: -15.149   log_lengthscale: 0.233   log_noise: -4.269\n"
      ]
     }
    ],
@@ -207,7 +207,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 2 Axes>"
       ]
diff --git a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb
index d4e7c630b..ee9fab38c 100644
--- a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb
+++ b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb
@@ -136,56 +136,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iter 1/50 - Loss: 110.857   log_lengthscale: -0.367   log_noise: -4.709\n",
-      "Iter 2/50 - Loss: 107.436   log_lengthscale: -0.439   log_noise: -4.709\n",
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+      "Iter 45/50 - Loss: -31.806   log_lengthscale: -1.462   log_noise: -4.705\n",
+      "Iter 46/50 - Loss: -31.099   log_lengthscale: -1.480   log_noise: -4.705\n",
+      "Iter 47/50 - Loss: -32.806   log_lengthscale: -1.506   log_noise: -4.705\n",
+      "Iter 48/50 - Loss: -33.661   log_lengthscale: -1.529   log_noise: -4.705\n",
+      "Iter 49/50 - Loss: -32.647   log_lengthscale: -1.549   log_noise: -4.705\n",
+      "Iter 50/50 - Loss: -36.437   log_lengthscale: -1.567   log_noise: -4.705\n"
      ]
     }
    ],
@@ -231,7 +231,7 @@
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 1008x720 with 6 Axes>"
       ]
diff --git a/examples/10_GP_Regression_Derivative_Information/index.rst b/examples/10_GP_Regression_Derivative_Information/index.rst
new file mode 100644
index 000000000..c4aa053ef
--- /dev/null
+++ b/examples/10_GP_Regression_Derivative_Information/index.rst
@@ -0,0 +1,9 @@
+.. mdinclude:: README.md
+
+.. toctree::
+   :glob:
+   :maxdepth: 1
+   :hidden:
+
+   Simple_GP_Regression_Derivative_Information_1d.ipynb
+   Simple_GP_Regression_Derivative_Information_2d.ipynb
diff --git a/gpytorch/kernels/rbf_kernel_grad.py b/gpytorch/kernels/rbf_kernel_grad.py
index 58312aaf4..5c4f45b31 100644
--- a/gpytorch/kernels/rbf_kernel_grad.py
+++ b/gpytorch/kernels/rbf_kernel_grad.py
@@ -1,16 +1,61 @@
 #!/usr/bin/env python3
 from .rbf_kernel import RBFKernel
-from ..lazy import DiagLazyTensor, NonLazyTensor
-from ..utils.deprecation import _deprecate_kwarg
 from torch.nn.functional import softplus
 import torch
 from ..lazy.kronecker_product_lazy_tensor import KroneckerProductLazyTensor
 
 
 class RBFKernelGrad(RBFKernel):
+    r"""
+    Computes a covariance matrix of the RBF kernel that models the covariance
+    between the values and partial derivatives for inputs :math:`\mathbf{x_1}`
+    and :math:`\mathbf{x_2}`.
+
+    See :class:`gpytorch.kernels.Kernel` for descriptions of the lengthscale options.
+
+    .. note::
+
+        This kernel does not have an `outputscale` parameter. To add a scaling parameter,
+        decorate this kernel with a :class:`gpytorch.kernels.ScaleKernel`.
+
+    Args:
+        :attr:`batch_size` (int, optional):
+            Set this if you want a separate lengthscale for each
+            batch of input data. It should be `b` if :attr:`x1` is a `b x n x d` tensor. Default: `1`.
+        :attr:`active_dims` (tuple of ints, optional):
+            Set this if you want to compute the covariance of only a few input dimensions. The ints
+            corresponds to the indices of the dimensions. Default: `None`.
+        :attr:`lengthscale_prior` (Prior, optional):
+            Set this if you want to apply a prior to the lengthscale parameter.  Default: `None`.
+        :attr:`param_transform` (function, optional):
+            Set this if you want to use something other than softplus to ensure positiveness of parameters.
+        :attr:`inv_param_transform` (function, optional):
+            Set this to allow setting parameters directly in transformed space and sampling from priors.
+            Automatically inferred for common transformations such as torch.exp or torch.nn.functional.softplus.
+        :attr:`eps` (float):
+            The minimum value that the lengthscale can take (prevents divide by zero errors). Default: `1e-6`.
+
+    Attributes:
+        :attr:`lengthscale` (Tensor):
+            The lengthscale parameter. Size/shape of parameter depends on the
+            :attr:`ard_num_dims` and :attr:`batch_size` arguments.
+
+    Example:
+        >>> x = torch.randn(10, 5)
+        >>> # Non-batch: Simple option
+        >>> covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernelGrad())
+        >>> covar = covar_module(x)  # Output: LazyTensor of size (60 x 60), where 60 = n * (d + 1)
+        >>>
+        >>> batch_x = torch.randn(2, 10, 5)
+        >>> # Batch: Simple option
+        >>> covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernelGrad())
+        >>> # Batch: different lengthscale for each batch
+        >>> covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernelGrad(batch_size=2))
+        >>> covar = covar_module(x)  # Output: LazyTensor of size (2 x 60 x 60)
+    """
+
     def __init__(
         self,
-        ard_num_dims=None,
         batch_size=1,
         active_dims=None,
         lengthscale_prior=None,
@@ -20,17 +65,13 @@ def __init__(
         **kwargs
     ):
         # TODO: Add support for ARD
-        if ard_num_dims is not None:
-            raise RuntimeError('ARD is not supported with derivative observations yet!')
-
         super(RBFKernelGrad, self).__init__(
-            ard_num_dims=None,
-            batch_size=1,
-            active_dims=None,
-            lengthscale_prior=None,
+            batch_size=batch_size,
+            active_dims=active_dims,
+            lengthscale_prior=lengthscale_prior,
             param_transform=softplus,
-            inv_param_transform=None,
-            eps=1e-6,
+            inv_param_transform=inv_param_transform,
+            eps=eps,
             **kwargs
         )
 
@@ -38,20 +79,21 @@ def forward(self, x1, x2, diag=False, **params):
         b = 1
         if len(x1.size()) == 2:
             n1, d = x1.size()
-            n2, _ = x2.size()
+            n2, d = x2.size()
         else:
             b, n1, d = x1.size()
             _, n2, _ = x2.size()
 
         K = torch.zeros(b, n1 * (d + 1), n2 * (d + 1))  # batch x n1(d+1) x n2(d+1)
+        ell = self.lengthscale.squeeze()
 
         if not diag:
-            ell = self.lengthscale
+            # Scale the inputs by the lengthscale (for stability)
             x1_ = x1 / ell
             x2_ = x2 / ell
 
-            # Form all possible rank-1 product for the gradient and Hessian blocks
-            outer = x1_.view([b, n1, 1, d]) - x2_.view([b, 1, n2, d])  
+            # Form all possible rank-1 products for the gradient and Hessian blocks
+            outer = x1_.view([b, n1, 1, d]) - x2_.view([b, 1, n2, d])
             outer = torch.transpose(outer, -1, -2).contiguous()
 
             # 1) Kernel block
@@ -60,23 +102,23 @@ def forward(self, x1, x2, diag=False, **params):
             K[..., :n1, :n2] = K_11
 
             # 2) First gradient block
-            outer1 = outer.view([b, n1, n2*d])
-            K[..., :n1, n2:] = (outer1  / ell) * K_11.repeat([1, 1, d])
+            outer1 = outer.view([b, n1, n2 * d]) / ell
+            K[..., :n1, n2:] = outer1 * K_11.repeat([1, 1, d])
 
             # 3) Second gradient block
-            outer2 = outer.transpose(-1, -3).contiguous().view([b, n2, n1*d])
-            outer2 = outer2.transpose(-1, -2) 
-            K[..., n1:, :n2] = - (outer2  / ell) * K_11.repeat([1, d, 1])
+            outer2 = outer.transpose(-1, -3).contiguous().view([b, n2, n1 * d])
+            outer2 = outer2.transpose(-1, -2) / ell
+            K[..., n1:, :n2] = -outer2 * K_11.repeat([1, d, 1])
 
             # 4) Hessian block
             outer3 = outer1.repeat([1, d, 1]) * outer2.repeat([1, 1, d])
             kp = KroneckerProductLazyTensor(torch.eye(d, d), torch.ones(n1, n2))
-            chain_rule = kp.evaluate() / ell.pow(2) - outer3 / ell.pow(2)
-            K[..., n1:, n2:] = chain_rule * K_11.repeat([1, d, d]) 
+            chain_rule = kp.evaluate() / ell.pow(2) - outer3
+            K[..., n1:, n2:] = chain_rule * K_11.repeat([1, d, d])
 
             # Symmetrize for stability
-            if n1 == n2 and torch.eq(x1, x2).all():  
-                K = 0.5*(K.transpose(-1, -2) + K)
+            if n1 == n2 and torch.eq(x1, x2).all():
+                K = 0.5 * (K.transpose(-1, -2) + K)
 
             # Apply a perfect shuffle permutation to match the MutiTask ordering
             pi1 = torch.arange(n1 * (d + 1)).view(d + 1, n1).t().contiguous().view((n1 * (d + 1)))
@@ -85,20 +127,20 @@ def forward(self, x1, x2, diag=False, **params):
 
             return K
 
-        else: # TODO: This will change when ARD is supported
+        else:  # TODO: This will change when ARD is supported
             if not (n1 == n2 and torch.eq(x1, x2).all()):
-                raise RuntimeError('diag=True only works when x1 == x2')
+                raise RuntimeError("diag=True only works when x1 == x2")
 
             kernel_diag = super(RBFKernelGrad, self).forward(x1, x2, diag=True)
-            grad_diag = (1 / self.lengthscale.pow(2)) * torch.ones(1, n2*d)
+            grad_diag = torch.ones(1, n2 * d) / (ell.pow(2))
             k_diag = torch.cat((kernel_diag, grad_diag), dim=-1)
             pi = torch.arange(n2 * (d + 1)).view(d + 1, n2).t().contiguous().view((n2 * (d + 1)))
             return k_diag[..., pi]
 
     def size(self, x1, x2):
         """
-        Given `n` data points in `d` dimensions, RBFKernelGrad returns an `n(d+1) x n(d+1)` kernel
-        matrix.
+        Given `x_1` with `n_1` data points and `x_2` with `n_2` data points, both in
+        `d` dimensions, RBFKernelGrad returns an `n_1(d+1) x n_2(d+1)` kernel matrix.
         """
         non_batch_size = ((x1.size(-1) + 1) * x1.size(-2), (x2.size(-1) + 1) * x2.size(-2))
         if x1.ndimension() == 3:
diff --git a/gpytorch/kernels/scale_kernel.py b/gpytorch/kernels/scale_kernel.py
index 4d7ba1d56..b6a4cc4d0 100644
--- a/gpytorch/kernels/scale_kernel.py
+++ b/gpytorch/kernels/scale_kernel.py
@@ -102,4 +102,4 @@ def forward(self, x1, x2, batch_dims=None, diag=False, **params):
         return orig_output.mul(outputscales)
 
     def size(self, x1, x2):
-        return self.base_kernel.size(x1, x2)
\ No newline at end of file
+        return self.base_kernel.size(x1, x2)
diff --git a/gpytorch/means/constant_mean_grad.py b/gpytorch/means/constant_mean_grad.py
index 2d134b8f4..f60989bb9 100644
--- a/gpytorch/means/constant_mean_grad.py
+++ b/gpytorch/means/constant_mean_grad.py
@@ -20,4 +20,3 @@ def forward(self, input):
             mean = self.constant.squeeze().repeat(input.size(0), input.size(1) + 1)
         mean[..., :, 1:] = 0
         return mean
-    
diff --git a/test/kernels/test_rbf_kernel_grad.py b/test/kernels/test_rbf_kernel_grad.py
index 6fbb52105..342950d7e 100644
--- a/test/kernels/test_rbf_kernel_grad.py
+++ b/test/kernels/test_rbf_kernel_grad.py
@@ -1,6 +1,5 @@
 #!/usr/bin/env python3
 
-import math
 import torch
 import unittest
 from gpytorch.kernels import RBFKernelGrad
@@ -13,14 +12,17 @@ def test_kernel(self):
 
         kernel = RBFKernelGrad()
         res = kernel(a, b).evaluate()
-        
-        actual = torch.tensor([
-            [0.35321, 0, -0.73517, 0.0054977, 0.011443, -0.022886],
-            [0, 0.73517, 0, -0.011443, -0.012374, 0.047633],
-            [0.73517, 0, -0.79499, 0.022886, 0.047633, -0.083824], 
-            [0.12476, 0.25967, 0.25967, 0.015565, 0.064793, 0],
-            [-0.25967, -0.2808, -0.54047, -0.064793, -0.23732, 0],
-            [-0.25967, -0.54047, -0.2808, 0, 0, 0.032396]])
+
+        actual = torch.tensor(
+            [
+                [0.35321, 0, -0.73517, 0.0054977, 0.011443, -0.022886],
+                [0, 0.73517, 0, -0.011443, -0.012374, 0.047633],
+                [0.73517, 0, -0.79499, 0.022886, 0.047633, -0.083824],
+                [0.12476, 0.25967, 0.25967, 0.015565, 0.064793, 0],
+                [-0.25967, -0.2808, -0.54047, -0.064793, -0.23732, 0],
+                [-0.25967, -0.54047, -0.2808, 0, 0, 0.032396],
+            ]
+        )
 
         self.assertLess(torch.norm(res - actual), 1e-5)
 
@@ -32,7 +34,7 @@ def test_kernel_batch(self):
         res = kernel(a, b).evaluate()
 
         # Compute each batch separately
-        actual = torch.zeros(2, 8, 8)        
+        actual = torch.zeros(2, 8, 8)
         actual[0, :, :] = kernel(a[0, :, :].squeeze(), b[0, :, :].squeeze()).evaluate()
         actual[1, :, :] = kernel(a[1, :, :].squeeze(), b[1, :, :].squeeze()).evaluate()
 
@@ -44,12 +46,12 @@ def test_initialize_lengthscale(self):
         actual_value = torch.tensor(3.14).view_as(kernel.lengthscale)
         self.assertLess(torch.norm(kernel.lengthscale - actual_value), 1e-5)
 
-    # def test_initialize_lengthscale_batch(self):
-    #     kernel = RBFKernelGrad(batch_size=2)
-    #     ls_init = torch.tensor([3.14, 4.13])
-    #     kernel.initialize(lengthscale=ls_init)
-    #     actual_value = ls_init.view_as(kernel.lengthscale)
-    #     self.assertLess(torch.norm(kernel.lengthscale - actual_value), 1e-5)
+    def test_initialize_lengthscale_batch(self):
+        kernel = RBFKernelGrad(batch_size=2)
+        ls_init = torch.tensor([3.14, 4.13])
+        kernel.initialize(lengthscale=ls_init)
+        actual_value = ls_init.view_as(kernel.lengthscale)
+        self.assertLess(torch.norm(kernel.lengthscale - actual_value), 1e-5)
 
 
 if __name__ == "__main__":

From a0ad6c03bdc19be392d31e5e04c187aa1d2635f1 Mon Sep 17 00:00:00 2001
From: dme65 <dme65@cornell.edu>
Date: Fri, 11 Jan 2019 14:47:46 -0500
Subject: [PATCH 11/25] Adding GPU support

---
 gpytorch/kernels/rbf_kernel_grad.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/gpytorch/kernels/rbf_kernel_grad.py b/gpytorch/kernels/rbf_kernel_grad.py
index 5c4f45b31..f195f5291 100644
--- a/gpytorch/kernels/rbf_kernel_grad.py
+++ b/gpytorch/kernels/rbf_kernel_grad.py
@@ -84,7 +84,7 @@ def forward(self, x1, x2, diag=False, **params):
             b, n1, d = x1.size()
             _, n2, _ = x2.size()
 
-        K = torch.zeros(b, n1 * (d + 1), n2 * (d + 1))  # batch x n1(d+1) x n2(d+1)
+        K = torch.zeros(b, n1 * (d + 1), n2 * (d + 1), device=x1.device, dtype=x1.dtype)  # batch x n1(d+1) x n2(d+1)
         ell = self.lengthscale.squeeze()
 
         if not diag:
@@ -112,7 +112,10 @@ def forward(self, x1, x2, diag=False, **params):
 
             # 4) Hessian block
             outer3 = outer1.repeat([1, d, 1]) * outer2.repeat([1, 1, d])
-            kp = KroneckerProductLazyTensor(torch.eye(d, d), torch.ones(n1, n2))
+            kp = KroneckerProductLazyTensor(
+                torch.eye(d, d, device=x1.device, dtype=x1.dtype),
+                torch.ones(n1, n2, device=x1.device, dtype=x1.dtype)
+            )
             chain_rule = kp.evaluate() / ell.pow(2) - outer3
             K[..., n1:, n2:] = chain_rule * K_11.repeat([1, d, d])
 
@@ -132,7 +135,7 @@ def forward(self, x1, x2, diag=False, **params):
                 raise RuntimeError("diag=True only works when x1 == x2")
 
             kernel_diag = super(RBFKernelGrad, self).forward(x1, x2, diag=True)
-            grad_diag = torch.ones(1, n2 * d) / (ell.pow(2))
+            grad_diag = torch.ones(1, n2 * d, device=x1.device, dtype=x1.dtype) / (ell.pow(2))
             k_diag = torch.cat((kernel_diag, grad_diag), dim=-1)
             pi = torch.arange(n2 * (d + 1)).view(d + 1, n2).t().contiguous().view((n2 * (d + 1)))
             return k_diag[..., pi]

From 1ede625ca747df956b2d27b08c6f810a81dc5a90 Mon Sep 17 00:00:00 2001
From: dme65 <dme65@cornell.edu>
Date: Fri, 11 Jan 2019 14:51:35 -0500
Subject: [PATCH 12/25] Adding CUDA test

---
 test/kernels/test_rbf_kernel_grad.py | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/test/kernels/test_rbf_kernel_grad.py b/test/kernels/test_rbf_kernel_grad.py
index 342950d7e..6e9803947 100644
--- a/test/kernels/test_rbf_kernel_grad.py
+++ b/test/kernels/test_rbf_kernel_grad.py
@@ -6,13 +6,10 @@
 
 
 class TestRBFKernelGrad(unittest.TestCase):
-    def test_kernel(self):
+    def test_kernel(self, cuda=False):
         a = torch.tensor([[[1, 2], [2, 4]]], dtype=torch.float)
         b = torch.tensor([[[1, 3], [0, 4]]], dtype=torch.float)
 
-        kernel = RBFKernelGrad()
-        res = kernel(a, b).evaluate()
-
         actual = torch.tensor(
             [
                 [0.35321, 0, -0.73517, 0.0054977, 0.011443, -0.022886],
@@ -24,8 +21,20 @@ def test_kernel(self):
             ]
         )
 
+        if cuda:
+            a = a.cuda()
+            b = b.cuda()
+            actual = actual.cuda()
+
+        kernel = RBFKernelGrad()
+        res = kernel(a, b).evaluate()
+
         self.assertLess(torch.norm(res - actual), 1e-5)
 
+    def test_kernel_cuda(self):
+        if torch.cuda.is_available():
+            self.test_kernel(cuda=True)
+
     def test_kernel_batch(self):
         a = torch.tensor([[[1, 2, 3], [2, 4, 0]], [[-1, 1, 2], [2, 1, 4]]], dtype=torch.float)
         b = torch.tensor([[[1, 3, 1]], [[2, -1, 0]]], dtype=torch.float).repeat(1, 2, 1)

From 11b5c1bffcaf7c9811b97d2aa47fb697bf234bf5 Mon Sep 17 00:00:00 2001
From: Jake Gardner <gardner.jake@gmail.com>
Date: Mon, 14 Jan 2019 13:44:19 -0500
Subject: [PATCH 13/25] JIT for distance computation, kernels can override with
 custom JIT scripts

---
 gpytorch/kernels/kernel.py       | 60 ++++++++++----------------------
 gpytorch/kernels/rbf_kernel.py   | 30 ++++++++++++++--
 test/kernels/test_grid_kernel.py |  2 +-
 3 files changed, 46 insertions(+), 46 deletions(-)

diff --git a/gpytorch/kernels/kernel.py b/gpytorch/kernels/kernel.py
index f98b5c77e..10ab1c5dc 100644
--- a/gpytorch/kernels/kernel.py
+++ b/gpytorch/kernels/kernel.py
@@ -10,7 +10,6 @@
 from ..utils.transforms import _get_inv_param_transform
 from torch.nn.functional import softplus
 from numpy import triu_indices
-import warnings
 
 
 class Kernel(Module):
@@ -193,29 +192,28 @@ def __pdist_dist(self, x1):
 
         return res
 
-    def __slow_sq_dist(self, x1, x2, diag, x1_eq_x2):
+    @torch.jit.script
+    def __jit_sq_dist(x1, x2, diag, x1_eq_x2):
         # Compute squared distance matrix using quadratic expansion
         x1_norm = x1.pow(2).sum(dim=-1, keepdim=True)
-        x2_norm = x2.pow(2).sum(dim=-1, keepdim=True)
-
-        if diag:
-            mid = (x1 * x2).sum(dim=-1, keepdim=True)
-            res = (x1_norm - 2 * mid + x2_norm).squeeze(-1)
+        if bool(x1_eq_x2):
+            x2_norm = x1_norm
         else:
-            mid = x1.matmul(x2.transpose(-2, -1))
-            res = x1_norm - 2 * mid + x2_norm.transpose(-2, -1)
+            x2_norm = x2.pow(2).sum(dim=-1, keepdim=True)
 
-        if x1_eq_x2:
+        res = x1.matmul(x2.transpose(-2, -1))
+        res = res.mul_(-2).add_(x2_norm.transpose(-2, -1)).add_(x1_norm)
+
+        if bool(x1_eq_x2):
             # Ensure zero diagonal
-            diag_inds = torch.arange(x1.shape[-2])
-            res[..., diag_inds, diag_inds] = 0
+            res.diagonal(dim1=-2, dim2=-1).fill_(0)
 
         # Zero out negative values
         res.clamp_min_(0)
 
         return res
 
-    def _covar_dist(self, x1, x2, diag=False, batch_dims=None, square_dist=False, **params):
+    def _covar_dist(self, x1, x2, diag=False, batch_dims=None, square_dist=False, jit_func=None, **params):
         """
         This is a helper method for computing the Euclidean distance between
         all pairs of points in x1 and x2.
@@ -262,35 +260,13 @@ def _covar_dist(self, x1, x2, diag=False, batch_dims=None, square_dist=False, **
                     res = (x1 - x2).pow(2).sum(-1)
                 else:
                     res = torch.norm(x1 - x2, p=2, dim=-1)
-
-        # TODO: Remove the size check when pytorch/15511 is fixed.
-        elif x1.size(-2) < 200 and x1_eq_x2:
-            # Full distance matrix in the square symmetric case
-            if x1.dim() == 3 and x1.shape[0] == 1:
-                # If we aren't in batch mode, we can always use torch.pdist
-                res = self.__pdist_dist(x1.squeeze(0)).unsqueeze(0)
-                res = res.pow(2) if square_dist else res
-            elif self.__pdist_supports_batch:
-                # torch.pdist still works on the latest pytorch-nightly
-                # TODO: This else branch is not needed on the next PyTorch release (> 1.0.0).
-                try:
-                    res = self.__pdist_dist(x1)
-                    res = res.pow(2) if square_dist else res
-                except RuntimeError as e:
-                    if 'pdist only supports 2D tensors, got:' in str(e):
-                        warnings.warn('You are using a version of PyTorch where torch.pdist does not support batch '
-                                      'matrices. Falling back on manual distance computation. Updating PyTorch to the '
-                                      'latest pytorch-nightly build will offer significant memory savings during kernel'
-                                      ' computation.')
-                        self.__pdist_supports_batch = False
-                    else:
-                        raise e
-
-        if res is None:
-            if not square_dist:
-                res = torch.norm(x1.unsqueeze(-2) - x2.unsqueeze(-3), p=2, dim=-1)
-            else:
-                res = self.__slow_sq_dist(x1, x2, diag, x1_eq_x2)
+        elif jit_func is not None:
+            res = jit_func(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2))
+        elif not square_dist:
+            # TODO: Use torch.pdist here when it isn't buggy.
+            res = torch.norm(x1.unsqueeze(-2) - x2.unsqueeze(-3), p=2, dim=-1)
+        else:
+            res = self.__jit_sq_dist(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2))
 
         if batch_dims == (0, 2):
             if diag:
diff --git a/gpytorch/kernels/rbf_kernel.py b/gpytorch/kernels/rbf_kernel.py
index 846045855..2374a8e3f 100644
--- a/gpytorch/kernels/rbf_kernel.py
+++ b/gpytorch/kernels/rbf_kernel.py
@@ -3,6 +3,7 @@
 from .kernel import Kernel
 from ..utils.deprecation import _deprecate_kwarg
 from torch.nn.functional import softplus
+import torch
 
 
 class RBFKernel(Kernel):
@@ -89,8 +90,31 @@ def __init__(
             eps=eps,
         )
 
-    def forward(self, x1, x2, **params):
+    @torch.jit.script
+    def __jit_rbf_kernel(x1, x2, diag, x1_eq_x2):
+        # Compute squared distance matrix using quadratic expansion
+        x1_norm = x1.pow(2).sum(dim=-1, keepdim=True)
+        if bool(x1_eq_x2):
+            x2_norm = x1_norm
+        else:
+            x2_norm = x2.pow(2).sum(dim=-1, keepdim=True)
+
+        res = x1.matmul(x2.transpose(-2, -1))
+        res = res.mul_(-2).add_(x2_norm.transpose(-2, -1)).add_(x1_norm)
+
+        if bool(x1_eq_x2):
+            # Ensure zero diagonal
+            res.diagonal(dim1=-2, dim2=-1).fill_(0)
+
+        # Zero out negative values
+        res.clamp_min_(0)
+
+        return res.div_(-2).exp_()
+
+    def forward(self, x1, x2, diag=False, **params):
         x1_ = x1.div(self.lengthscale)
         x2_ = x2.div(self.lengthscale)
-        diff = self._covar_dist(x1_, x2_, square_dist=True, **params)
-        return diff.div_(-2).exp_()
+        diff = self._covar_dist(x1_, x2_, square_dist=True, diag=diag, jit_func=self.__jit_rbf_kernel, **params)
+        if diag:
+            diff.div_(-2).exp_()
+        return diff
diff --git a/test/kernels/test_grid_kernel.py b/test/kernels/test_grid_kernel.py
index 5aab2d524..42a97b62c 100644
--- a/test/kernels/test_grid_kernel.py
+++ b/test/kernels/test_grid_kernel.py
@@ -28,7 +28,7 @@ def test_grid_grid(self):
         self.assertIsInstance(grid_covar, KroneckerProductLazyTensor)
         grid_eval = kernel(grid_data, grid_data).evaluate()
         actual_eval = base_kernel(grid_data, grid_data).evaluate()
-        self.assertLess(torch.norm(grid_eval - actual_eval), 1e-5)
+        self.assertLess(torch.norm(grid_eval - actual_eval), 1.2e-5)
 
     def test_nongrid_grid(self):
         base_kernel = RBFKernel()

From a1e8bcc26a4b432776620b242023adb7acf206e3 Mon Sep 17 00:00:00 2001
From: Jake Gardner <gardner.jake@gmail.com>
Date: Sat, 19 Jan 2019 15:54:04 -0500
Subject: [PATCH 14/25] JIT script internals of Linear CG

---
 gpytorch/utils/linear_cg.py | 128 ++++++++++++++++++++++++++----------
 1 file changed, 95 insertions(+), 33 deletions(-)

diff --git a/gpytorch/utils/linear_cg.py b/gpytorch/utils/linear_cg.py
index 9803d1039..1ff6f816e 100644
--- a/gpytorch/utils/linear_cg.py
+++ b/gpytorch/utils/linear_cg.py
@@ -7,6 +7,58 @@
 def _default_preconditioner(x):
     return x.clone()
 
+@torch.jit.script
+def _jit_linear_cg_updates_no_precond(
+    mvms, result, alpha, residual_inner_prod, eps, beta, residual, precond_residual, mul_storage, curr_conjugate_vec
+):
+    torch.mul(curr_conjugate_vec, mvms, out=mul_storage)
+    torch.sum(mul_storage, dim=-2, keepdim=True, out=alpha)
+    alpha.add_(eps)
+    torch.div(residual_inner_prod, alpha, out=alpha)
+
+    # Update residual
+    # residual_{k} = residual_{k-1} - alpha_{k} mat p_vec_{k-1}
+    torch.addcmul(residual, -alpha, mvms, out=residual)
+
+    # Update precond_residual
+    # precon_residual{k} = M^-1 residual_{k}
+    precond_residual = residual.clone() # preconditioner(residual)
+
+    # # Update result
+    # # result_{k} = result_{k-1} + alpha_{k} p_vec_{k-1}
+    torch.addcmul(result, alpha, curr_conjugate_vec, out=result)
+
+    # beta_{k} = (precon_residual{k}^T r_vec_{k}) / (precon_residual{k-1}^T r_vec_{k-1})
+    residual_inner_prod.add_(eps)
+    torch.reciprocal(residual_inner_prod, out=beta)
+    torch.mul(residual, precond_residual, out=mul_storage)
+    torch.sum(mul_storage, dim=-2, keepdim=True, out=residual_inner_prod)
+    beta.mul_(residual_inner_prod)
+
+    # Update curr_conjugate_vec
+    # curr_conjugate_vec_{k} = precon_residual{k} + beta_{k} curr_conjugate_vec_{k-1}
+    curr_conjugate_vec.mul_(beta).add_(precond_residual)
+
+
+@torch.jit.script
+def _jit_linear_cg_updates(
+    result, alpha, residual_inner_prod, eps, beta, residual, precond_residual, mul_storage, curr_conjugate_vec
+):
+    # # Update result
+    # # result_{k} = result_{k-1} + alpha_{k} p_vec_{k-1}
+    torch.addcmul(result, alpha, curr_conjugate_vec, out=result)
+
+    # beta_{k} = (precon_residual{k}^T r_vec_{k}) / (precon_residual{k-1}^T r_vec_{k-1})
+    residual_inner_prod.add_(eps)
+    torch.reciprocal(residual_inner_prod, out=beta)
+    torch.mul(residual, precond_residual, out=mul_storage)
+    torch.sum(mul_storage, -2, keepdim=True, out=residual_inner_prod)
+    beta.mul_(residual_inner_prod)
+
+    # Update curr_conjugate_vec
+    # curr_conjugate_vec_{k} = precon_residual{k} + beta_{k} curr_conjugate_vec_{k-1}
+    curr_conjugate_vec.mul_(beta).add_(precond_residual)
+
 
 def linear_cg(
     matmul_closure,
@@ -55,6 +107,9 @@ def linear_cg(
         initial_guess = torch.zeros_like(rhs)
     if preconditioner is None:
         preconditioner = _default_preconditioner
+        no_precond = True
+    else:
+        no_precond = False
 
     # If we are running m CG iterations, we obviously can't get more than m Lanczos coefficients
     if max_tridiag_iter > max_iter:
@@ -115,39 +170,46 @@ def linear_cg(
         # Get next alpha
         # alpha_{k} = (residual_{k-1}^T precon_residual{k-1}) / (p_vec_{k-1}^T mat p_vec_{k-1})
         mvms = matmul_closure(curr_conjugate_vec)
-        torch.mul(curr_conjugate_vec, mvms, out=mul_storage)
-        torch.sum(mul_storage, -2, keepdim=True, out=alpha)
-        alpha.add_(eps)
-        torch.div(residual_inner_prod, alpha, out=alpha)
-
-        # Update result
-        # result_{k} = result_{k-1} + alpha_{k} p_vec_{k-1}
-        torch.addcmul(result, alpha, curr_conjugate_vec, out=result)
-
-        # Update residual
-        # residual_{k} = residual_{k-1} - alpha_{k} mat p_vec_{k-1}
-        torch.addcmul(residual, -1, alpha, mvms, out=residual)
-
-        # If residual are sufficiently small, then exit loop
-        # Alternatively, exit if this is our last iteration
-        torch.norm(residual, 2, dim=-2, out=residual_norm)
-        if (residual_norm < tolerance).all() and not (n_tridiag and k < n_tridiag_iter):
-            break
-
-        # Update precond_residual
-        # precon_residual{k} = M^-1 residual_{k}
-        precond_residual = preconditioner(residual)
-
-        # beta_{k} = (precon_residual{k}^T r_vec_{k}) / (precon_residual{k-1}^T r_vec_{k-1})
-        residual_inner_prod.add_(eps)
-        torch.reciprocal(residual_inner_prod, out=beta)
-        torch.mul(residual, precond_residual, out=mul_storage)
-        torch.sum(mul_storage, -2, keepdim=True, out=residual_inner_prod)
-        beta.mul_(residual_inner_prod)
-
-        # Update curr_conjugate_vec
-        # curr_conjugate_vec_{k} = precon_residual{k} + beta_{k} curr_conjugate_vec_{k-1}
-        curr_conjugate_vec.mul_(beta).add_(precond_residual)
+        if not no_precond:
+            torch.mul(curr_conjugate_vec, mvms, out=mul_storage)
+            torch.sum(mul_storage, -2, keepdim=True, out=alpha)
+            alpha.add_(eps)
+            torch.div(residual_inner_prod, alpha, out=alpha)
+
+            # Update residual
+            # residual_{k} = residual_{k-1} - alpha_{k} mat p_vec_{k-1}
+            torch.addcmul(residual, -1, alpha, mvms, out=residual)
+
+            # Update precond_residual
+            # precon_residual{k} = M^-1 residual_{k}
+            precond_residual = preconditioner(residual)
+
+            _jit_linear_cg_updates(
+                result,
+                alpha,
+                residual_inner_prod,
+                torch.tensor(eps),
+                beta,
+                residual,
+                precond_residual,
+                mul_storage,
+                curr_conjugate_vec,
+            )
+        else:
+            _jit_linear_cg_updates_no_precond(
+                mvms,
+                result,
+                alpha,
+                residual_inner_prod,
+                torch.tensor(eps),
+                beta,
+                residual,
+                precond_residual,
+                mul_storage,
+                curr_conjugate_vec,
+            )
+        # # if (residual_norm < tolerance).all() and not (n_tridiag and k < n_tridiag_iter):
+        # #     break
 
         # Update tridiagonal matrices, if applicable
         if n_tridiag and k < n_tridiag_iter and update_tridiag:

From b33a3967f682a765595eb5d401f1f740f380a13b Mon Sep 17 00:00:00 2001
From: Jake Gardner <gardner.jake@gmail.com>
Date: Tue, 22 Jan 2019 16:09:21 -0500
Subject: [PATCH 15/25] Matern kernel fix

---
 gpytorch/kernels/kernel.py  | 6 ++++--
 gpytorch/utils/linear_cg.py | 5 ++---
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/gpytorch/kernels/kernel.py b/gpytorch/kernels/kernel.py
index 10ab1c5dc..43aa676bc 100644
--- a/gpytorch/kernels/kernel.py
+++ b/gpytorch/kernels/kernel.py
@@ -263,8 +263,10 @@ def _covar_dist(self, x1, x2, diag=False, batch_dims=None, square_dist=False, ji
         elif jit_func is not None:
             res = jit_func(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2))
         elif not square_dist:
-            # TODO: Use torch.pdist here when it isn't buggy.
-            res = torch.norm(x1.unsqueeze(-2) - x2.unsqueeze(-3), p=2, dim=-1)
+            if x1_eq_x2:
+                res = self.__jit_sq_dist(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2)).clamp_min_(1e-30).sqrt_()
+            else:
+                res = torch.norm(x1.unsqueeze(-2) - x2.unsqueeze(-3), p=2, dim=-1)
         else:
             res = self.__jit_sq_dist(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2))
 
diff --git a/gpytorch/utils/linear_cg.py b/gpytorch/utils/linear_cg.py
index 1ff6f816e..a0f81e2e5 100644
--- a/gpytorch/utils/linear_cg.py
+++ b/gpytorch/utils/linear_cg.py
@@ -7,6 +7,7 @@
 def _default_preconditioner(x):
     return x.clone()
 
+
 @torch.jit.script
 def _jit_linear_cg_updates_no_precond(
     mvms, result, alpha, residual_inner_prod, eps, beta, residual, precond_residual, mul_storage, curr_conjugate_vec
@@ -22,7 +23,7 @@ def _jit_linear_cg_updates_no_precond(
 
     # Update precond_residual
     # precon_residual{k} = M^-1 residual_{k}
-    precond_residual = residual.clone() # preconditioner(residual)
+    precond_residual = residual.clone()
 
     # # Update result
     # # result_{k} = result_{k-1} + alpha_{k} p_vec_{k-1}
@@ -208,8 +209,6 @@ def linear_cg(
                 mul_storage,
                 curr_conjugate_vec,
             )
-        # # if (residual_norm < tolerance).all() and not (n_tridiag and k < n_tridiag_iter):
-        # #     break
 
         # Update tridiagonal matrices, if applicable
         if n_tridiag and k < n_tridiag_iter and update_tridiag:

From 80864e19dcb6575b990434ab9a216ae4f948cd84 Mon Sep 17 00:00:00 2001
From: Jake <jrg365@cornell.edu>
Date: Wed, 23 Jan 2019 14:36:09 -0500
Subject: [PATCH 16/25] Code reuse for JIT scripts

---
 gpytorch/kernels/kernel.py     | 47 ++++++++++++++++----------------
 gpytorch/kernels/rbf_kernel.py | 19 ++-----------
 gpytorch/utils/linear_cg.py    | 49 +++++++++++++---------------------
 3 files changed, 45 insertions(+), 70 deletions(-)

diff --git a/gpytorch/kernels/kernel.py b/gpytorch/kernels/kernel.py
index 43aa676bc..9953d0e59 100644
--- a/gpytorch/kernels/kernel.py
+++ b/gpytorch/kernels/kernel.py
@@ -12,6 +12,28 @@
 from numpy import triu_indices
 
 
+@torch.jit.script
+def _jit_sq_dist(x1, x2, diag, x1_eq_x2):
+    # Compute squared distance matrix using quadratic expansion
+    x1_norm = x1.pow(2).sum(dim=-1, keepdim=True)
+    if bool(x1_eq_x2):
+        x2_norm = x1_norm
+    else:
+        x2_norm = x2.pow(2).sum(dim=-1, keepdim=True)
+
+    res = x1.matmul(x2.transpose(-2, -1))
+    res = res.mul_(-2).add_(x2_norm.transpose(-2, -1)).add_(x1_norm)
+
+    if bool(x1_eq_x2):
+        # Ensure zero diagonal
+        res.diagonal(dim1=-2, dim2=-1).fill_(0)
+
+    # Zero out negative values
+    res.clamp_min_(0)
+
+    return res
+
+
 class Kernel(Module):
     """
     Kernels in GPyTorch are implemented as a :class:`gpytorch.Module` that, when called on two :obj:`torch.tensor`
@@ -192,27 +214,6 @@ def __pdist_dist(self, x1):
 
         return res
 
-    @torch.jit.script
-    def __jit_sq_dist(x1, x2, diag, x1_eq_x2):
-        # Compute squared distance matrix using quadratic expansion
-        x1_norm = x1.pow(2).sum(dim=-1, keepdim=True)
-        if bool(x1_eq_x2):
-            x2_norm = x1_norm
-        else:
-            x2_norm = x2.pow(2).sum(dim=-1, keepdim=True)
-
-        res = x1.matmul(x2.transpose(-2, -1))
-        res = res.mul_(-2).add_(x2_norm.transpose(-2, -1)).add_(x1_norm)
-
-        if bool(x1_eq_x2):
-            # Ensure zero diagonal
-            res.diagonal(dim1=-2, dim2=-1).fill_(0)
-
-        # Zero out negative values
-        res.clamp_min_(0)
-
-        return res
-
     def _covar_dist(self, x1, x2, diag=False, batch_dims=None, square_dist=False, jit_func=None, **params):
         """
         This is a helper method for computing the Euclidean distance between
@@ -264,11 +265,11 @@ def _covar_dist(self, x1, x2, diag=False, batch_dims=None, square_dist=False, ji
             res = jit_func(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2))
         elif not square_dist:
             if x1_eq_x2:
-                res = self.__jit_sq_dist(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2)).clamp_min_(1e-30).sqrt_()
+                res = _jit_sq_dist(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2)).clamp_min_(1e-30).sqrt_()
             else:
                 res = torch.norm(x1.unsqueeze(-2) - x2.unsqueeze(-3), p=2, dim=-1)
         else:
-            res = self.__jit_sq_dist(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2))
+            res = _jit_sq_dist(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2))
 
         if batch_dims == (0, 2):
             if diag:
diff --git a/gpytorch/kernels/rbf_kernel.py b/gpytorch/kernels/rbf_kernel.py
index 2374a8e3f..b667b81fa 100644
--- a/gpytorch/kernels/rbf_kernel.py
+++ b/gpytorch/kernels/rbf_kernel.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 
 from .kernel import Kernel
+from .kernel import _jit_sq_dist
 from ..utils.deprecation import _deprecate_kwarg
 from torch.nn.functional import softplus
 import torch
@@ -92,23 +93,7 @@ def __init__(
 
     @torch.jit.script
     def __jit_rbf_kernel(x1, x2, diag, x1_eq_x2):
-        # Compute squared distance matrix using quadratic expansion
-        x1_norm = x1.pow(2).sum(dim=-1, keepdim=True)
-        if bool(x1_eq_x2):
-            x2_norm = x1_norm
-        else:
-            x2_norm = x2.pow(2).sum(dim=-1, keepdim=True)
-
-        res = x1.matmul(x2.transpose(-2, -1))
-        res = res.mul_(-2).add_(x2_norm.transpose(-2, -1)).add_(x1_norm)
-
-        if bool(x1_eq_x2):
-            # Ensure zero diagonal
-            res.diagonal(dim1=-2, dim2=-1).fill_(0)
-
-        # Zero out negative values
-        res.clamp_min_(0)
-
+        res = _jit_sq_dist(x1, x2, diag, x1_eq_x2)
         return res.div_(-2).exp_()
 
     def forward(self, x1, x2, diag=False, **params):
diff --git a/gpytorch/utils/linear_cg.py b/gpytorch/utils/linear_cg.py
index a0f81e2e5..f234dd700 100644
--- a/gpytorch/utils/linear_cg.py
+++ b/gpytorch/utils/linear_cg.py
@@ -9,22 +9,9 @@ def _default_preconditioner(x):
 
 
 @torch.jit.script
-def _jit_linear_cg_updates_no_precond(
-    mvms, result, alpha, residual_inner_prod, eps, beta, residual, precond_residual, mul_storage, curr_conjugate_vec
+def _jit_linear_cg_updates(
+    result, alpha, residual_inner_prod, eps, beta, residual, precond_residual, mul_storage, curr_conjugate_vec
 ):
-    torch.mul(curr_conjugate_vec, mvms, out=mul_storage)
-    torch.sum(mul_storage, dim=-2, keepdim=True, out=alpha)
-    alpha.add_(eps)
-    torch.div(residual_inner_prod, alpha, out=alpha)
-
-    # Update residual
-    # residual_{k} = residual_{k-1} - alpha_{k} mat p_vec_{k-1}
-    torch.addcmul(residual, -alpha, mvms, out=residual)
-
-    # Update precond_residual
-    # precon_residual{k} = M^-1 residual_{k}
-    precond_residual = residual.clone()
-
     # # Update result
     # # result_{k} = result_{k-1} + alpha_{k} p_vec_{k-1}
     torch.addcmul(result, alpha, curr_conjugate_vec, out=result)
@@ -33,7 +20,7 @@ def _jit_linear_cg_updates_no_precond(
     residual_inner_prod.add_(eps)
     torch.reciprocal(residual_inner_prod, out=beta)
     torch.mul(residual, precond_residual, out=mul_storage)
-    torch.sum(mul_storage, dim=-2, keepdim=True, out=residual_inner_prod)
+    torch.sum(mul_storage, -2, keepdim=True, out=residual_inner_prod)
     beta.mul_(residual_inner_prod)
 
     # Update curr_conjugate_vec
@@ -42,23 +29,25 @@ def _jit_linear_cg_updates_no_precond(
 
 
 @torch.jit.script
-def _jit_linear_cg_updates(
-    result, alpha, residual_inner_prod, eps, beta, residual, precond_residual, mul_storage, curr_conjugate_vec
+def _jit_linear_cg_updates_no_precond(
+    mvms, result, alpha, residual_inner_prod, eps, beta, residual, precond_residual, mul_storage, curr_conjugate_vec
 ):
-    # # Update result
-    # # result_{k} = result_{k-1} + alpha_{k} p_vec_{k-1}
-    torch.addcmul(result, alpha, curr_conjugate_vec, out=result)
+    torch.mul(curr_conjugate_vec, mvms, out=mul_storage)
+    torch.sum(mul_storage, dim=-2, keepdim=True, out=alpha)
+    alpha.add_(eps)
+    torch.div(residual_inner_prod, alpha, out=alpha)
 
-    # beta_{k} = (precon_residual{k}^T r_vec_{k}) / (precon_residual{k-1}^T r_vec_{k-1})
-    residual_inner_prod.add_(eps)
-    torch.reciprocal(residual_inner_prod, out=beta)
-    torch.mul(residual, precond_residual, out=mul_storage)
-    torch.sum(mul_storage, -2, keepdim=True, out=residual_inner_prod)
-    beta.mul_(residual_inner_prod)
+    # Update residual
+    # residual_{k} = residual_{k-1} - alpha_{k} mat p_vec_{k-1}
+    torch.addcmul(residual, -alpha, mvms, out=residual)
 
-    # Update curr_conjugate_vec
-    # curr_conjugate_vec_{k} = precon_residual{k} + beta_{k} curr_conjugate_vec_{k-1}
-    curr_conjugate_vec.mul_(beta).add_(precond_residual)
+    # Update precond_residual
+    # precon_residual{k} = M^-1 residual_{k}
+    precond_residual = residual.clone()
+
+    _jit_linear_cg_updates(
+        result, alpha, residual_inner_prod, eps, beta, residual, precond_residual, mul_storage, curr_conjugate_vec
+    )
 
 
 def linear_cg(

From 0ff64169766bd22d1d8cae35ef0ab3f0d6f16e2e Mon Sep 17 00:00:00 2001
From: Jake <jrg365@cornell.edu>
Date: Wed, 23 Jan 2019 15:17:11 -0500
Subject: [PATCH 17/25] Kernels now pass scripts for handling postprocessing
 only

---
 gpytorch/kernels/kernel.py     | 66 ++++++++++++++++++++++------------
 gpytorch/kernels/rbf_kernel.py | 13 ++++---
 2 files changed, 49 insertions(+), 30 deletions(-)

diff --git a/gpytorch/kernels/kernel.py b/gpytorch/kernels/kernel.py
index 9953d0e59..6729d770d 100644
--- a/gpytorch/kernels/kernel.py
+++ b/gpytorch/kernels/kernel.py
@@ -13,25 +13,44 @@
 
 
 @torch.jit.script
-def _jit_sq_dist(x1, x2, diag, x1_eq_x2):
-    # Compute squared distance matrix using quadratic expansion
-    x1_norm = x1.pow(2).sum(dim=-1, keepdim=True)
-    if bool(x1_eq_x2):
-        x2_norm = x1_norm
-    else:
-        x2_norm = x2.pow(2).sum(dim=-1, keepdim=True)
+def default_postprocess_script(x):
+    return x
 
-    res = x1.matmul(x2.transpose(-2, -1))
-    res = res.mul_(-2).add_(x2_norm.transpose(-2, -1)).add_(x1_norm)
 
-    if bool(x1_eq_x2):
-        # Ensure zero diagonal
-        res.diagonal(dim1=-2, dim2=-1).fill_(0)
+class Distance(torch.jit.ScriptModule):
+    def __init__(self, postprocess_script=None):
+        super().__init__()
+        if postprocess_script is not None:
+            self._postprocess = postprocess_script
+        else:
+            self._postprocess = default_postprocess_script
+
+    @torch.jit.script_method
+    def _jit_sq_dist(self, x1, x2, diag, x1_eq_x2):
+        # Compute squared distance matrix using quadratic expansion
+        x1_norm = x1.pow(2).sum(dim=-1, keepdim=True)
+        if bool(x1_eq_x2):
+            x2_norm = x1_norm
+        else:
+            x2_norm = x2.pow(2).sum(dim=-1, keepdim=True)
+
+        res = x1.matmul(x2.transpose(-2, -1))
+        res = res.mul_(-2).add_(x2_norm.transpose(-2, -1)).add_(x1_norm)
+
+        if bool(x1_eq_x2):
+            # Ensure zero diagonal
+            res.diagonal(dim1=-2, dim2=-1).fill_(0)
+
+        # Zero out negative values
+        res.clamp_min_(0)
 
-    # Zero out negative values
-    res.clamp_min_(0)
+        return self._postprocess(res)
 
-    return res
+    @torch.jit.script_method
+    def _jit_dist(self, x1, x2, diag, x1_eq_x2):
+        res = self._jit_sq_dist(x1, x2, diag, x1_eq_x2)
+        res = res.clamp_min_(1e-30).sqrt_()
+        return self._postprocess(res)
 
 
 class Kernel(Module):
@@ -214,7 +233,11 @@ def __pdist_dist(self, x1):
 
         return res
 
-    def _covar_dist(self, x1, x2, diag=False, batch_dims=None, square_dist=False, jit_func=None, **params):
+    @torch.jit.script_method
+    def _postprocess(self, dist_mat):
+        return dist_mat
+
+    def _covar_dist(self, x1, x2, diag=False, batch_dims=None, square_dist=False, postprocess_func=None, **params):
         """
         This is a helper method for computing the Euclidean distance between
         all pairs of points in x1 and x2.
@@ -247,6 +270,8 @@ def _covar_dist(self, x1, x2, diag=False, batch_dims=None, square_dist=False, ji
 
         res = None
 
+        distance_module = Distance(postprocess_func)
+
         if diag:
             # Special case the diagonal because we can return all zeros most of the time.
             if x1_eq_x2:
@@ -261,15 +286,10 @@ def _covar_dist(self, x1, x2, diag=False, batch_dims=None, square_dist=False, ji
                     res = (x1 - x2).pow(2).sum(-1)
                 else:
                     res = torch.norm(x1 - x2, p=2, dim=-1)
-        elif jit_func is not None:
-            res = jit_func(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2))
         elif not square_dist:
-            if x1_eq_x2:
-                res = _jit_sq_dist(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2)).clamp_min_(1e-30).sqrt_()
-            else:
-                res = torch.norm(x1.unsqueeze(-2) - x2.unsqueeze(-3), p=2, dim=-1)
+            res = distance_module._jit_dist(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2))
         else:
-            res = _jit_sq_dist(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2))
+            res = distance_module._jit_sq_dist(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2))
 
         if batch_dims == (0, 2):
             if diag:
diff --git a/gpytorch/kernels/rbf_kernel.py b/gpytorch/kernels/rbf_kernel.py
index b667b81fa..2462c3e6d 100644
--- a/gpytorch/kernels/rbf_kernel.py
+++ b/gpytorch/kernels/rbf_kernel.py
@@ -1,12 +1,16 @@
 #!/usr/bin/env python3
 
 from .kernel import Kernel
-from .kernel import _jit_sq_dist
 from ..utils.deprecation import _deprecate_kwarg
 from torch.nn.functional import softplus
 import torch
 
 
+@torch.jit.script
+def postprocess_rbf(dist_mat):
+    return dist_mat.div_(-2).exp_()
+
+
 class RBFKernel(Kernel):
     r"""
     Computes a covariance matrix based on the RBF (squared exponential) kernel
@@ -91,15 +95,10 @@ def __init__(
             eps=eps,
         )
 
-    @torch.jit.script
-    def __jit_rbf_kernel(x1, x2, diag, x1_eq_x2):
-        res = _jit_sq_dist(x1, x2, diag, x1_eq_x2)
-        return res.div_(-2).exp_()
-
     def forward(self, x1, x2, diag=False, **params):
         x1_ = x1.div(self.lengthscale)
         x2_ = x2.div(self.lengthscale)
-        diff = self._covar_dist(x1_, x2_, square_dist=True, diag=diag, jit_func=self.__jit_rbf_kernel, **params)
+        diff = self._covar_dist(x1_, x2_, square_dist=True, diag=diag, postprocess_func=postprocess_rbf, **params)
         if diag:
             diff.div_(-2).exp_()
         return diff

From 8e7d3eddc29b93d40e48b07d9eb26a7e5015199d Mon Sep 17 00:00:00 2001
From: Jake <jrg365@cornell.edu>
Date: Thu, 24 Jan 2019 12:39:29 -0500
Subject: [PATCH 18/25] Call postprocess in diag mode

---
 gpytorch/kernels/kernel.py     | 22 +++++++++++++++-------
 gpytorch/kernels/rbf_kernel.py |  2 --
 2 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/gpytorch/kernels/kernel.py b/gpytorch/kernels/kernel.py
index 6729d770d..2fa2cecb6 100644
--- a/gpytorch/kernels/kernel.py
+++ b/gpytorch/kernels/kernel.py
@@ -18,12 +18,9 @@ def default_postprocess_script(x):
 
 
 class Distance(torch.jit.ScriptModule):
-    def __init__(self, postprocess_script=None):
+    def __init__(self, postprocess_script=default_postprocess_script):
         super().__init__()
-        if postprocess_script is not None:
-            self._postprocess = postprocess_script
-        else:
-            self._postprocess = default_postprocess_script
+        self._postprocess = postprocess_script
 
     @torch.jit.script_method
     def _jit_sq_dist(self, x1, x2, diag, x1_eq_x2):
@@ -237,7 +234,16 @@ def __pdist_dist(self, x1):
     def _postprocess(self, dist_mat):
         return dist_mat
 
-    def _covar_dist(self, x1, x2, diag=False, batch_dims=None, square_dist=False, postprocess_func=None, **params):
+    def _covar_dist(
+        self,
+        x1,
+        x2,
+        diag=False,
+        batch_dims=None,
+        square_dist=False,
+        postprocess_func=default_postprocess_script,
+        **params
+    ):
         """
         This is a helper method for computing the Euclidean distance between
         all pairs of points in x1 and x2.
@@ -280,12 +286,14 @@ def _covar_dist(self, x1, x2, diag=False, batch_dims=None, square_dist=False, po
                     res = torch.zeros(x1.shape[0] * x1.shape[-1], x2.shape[-2], dtype=x1.dtype, device=x1.device)
                 else:
                     res = torch.zeros(x1.shape[0], x2.shape[-2], dtype=x1.dtype, device=x1.device)
-                return res
+                return postprocess_func(res)
             else:
                 if square_dist:
                     res = (x1 - x2).pow(2).sum(-1)
                 else:
                     res = torch.norm(x1 - x2, p=2, dim=-1)
+
+            res = postprocess_func(res)
         elif not square_dist:
             res = distance_module._jit_dist(x1, x2, torch.tensor(diag), torch.tensor(x1_eq_x2))
         else:
diff --git a/gpytorch/kernels/rbf_kernel.py b/gpytorch/kernels/rbf_kernel.py
index 2462c3e6d..d0b35fd24 100644
--- a/gpytorch/kernels/rbf_kernel.py
+++ b/gpytorch/kernels/rbf_kernel.py
@@ -99,6 +99,4 @@ def forward(self, x1, x2, diag=False, **params):
         x1_ = x1.div(self.lengthscale)
         x2_ = x2.div(self.lengthscale)
         diff = self._covar_dist(x1_, x2_, square_dist=True, diag=diag, postprocess_func=postprocess_rbf, **params)
-        if diag:
-            diff.div_(-2).exp_()
         return diff

From 1f88504ac27a10eb74766a1b9ae8b66d72d602b6 Mon Sep 17 00:00:00 2001
From: Jake <jrg365@cornell.edu>
Date: Thu, 24 Jan 2019 12:41:42 -0500
Subject: [PATCH 19/25] Remove double negative variable name

---
 gpytorch/utils/linear_cg.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/gpytorch/utils/linear_cg.py b/gpytorch/utils/linear_cg.py
index f234dd700..8c01aa7e1 100644
--- a/gpytorch/utils/linear_cg.py
+++ b/gpytorch/utils/linear_cg.py
@@ -97,9 +97,9 @@ def linear_cg(
         initial_guess = torch.zeros_like(rhs)
     if preconditioner is None:
         preconditioner = _default_preconditioner
-        no_precond = True
+        precond = False
     else:
-        no_precond = False
+        precond = True
 
     # If we are running m CG iterations, we obviously can't get more than m Lanczos coefficients
     if max_tridiag_iter > max_iter:
@@ -160,7 +160,7 @@ def linear_cg(
         # Get next alpha
         # alpha_{k} = (residual_{k-1}^T precon_residual{k-1}) / (p_vec_{k-1}^T mat p_vec_{k-1})
         mvms = matmul_closure(curr_conjugate_vec)
-        if not no_precond:
+        if precond:
             torch.mul(curr_conjugate_vec, mvms, out=mul_storage)
             torch.sum(mul_storage, -2, keepdim=True, out=alpha)
             alpha.add_(eps)

From 6faba6ad0168c32a9c3a734834dcc6f0787855c3 Mon Sep 17 00:00:00 2001
From: Jake Gardner <gardner.jake@gmail.com>
Date: Tue, 22 Jan 2019 22:07:06 -0500
Subject: [PATCH 20/25] Implement Gauss-Hermite quadrature for use with
 likelihoods

---
 docs/source/utils.rst                        |   6 +
 gpytorch/likelihoods/bernoulli_likelihood.py |  13 +-
 gpytorch/settings.py                         |  10 ++
 gpytorch/utils/__init__.py                   |   2 +
 gpytorch/utils/quadrature.py                 |  63 +++++++++
 test/utils/test_quadrature.py                | 137 +++++++++++++++++++
 6 files changed, 225 insertions(+), 6 deletions(-)
 create mode 100644 gpytorch/utils/quadrature.py
 create mode 100644 test/utils/test_quadrature.py

diff --git a/docs/source/utils.rst b/docs/source/utils.rst
index dcf63af5c..86354584f 100644
--- a/docs/source/utils.rst
+++ b/docs/source/utils.rst
@@ -25,6 +25,12 @@ Pivoted Cholesky Utilities
 .. automodule:: gpytorch.utils.pivoted_cholesky
    :members:
 
+Quadrature Utilities
+~~~~~~~~~~~~~~~~~~~~
+
+.. automodule:: gpytorch.utils.quadrature
+   :members:
+
 Sparse Utilities
 ~~~~~~~~~~~~~~~~~
 
diff --git a/gpytorch/likelihoods/bernoulli_likelihood.py b/gpytorch/likelihoods/bernoulli_likelihood.py
index 20dafceb1..47fe3544f 100755
--- a/gpytorch/likelihoods/bernoulli_likelihood.py
+++ b/gpytorch/likelihoods/bernoulli_likelihood.py
@@ -2,9 +2,8 @@
 
 import torch
 from torch.distributions import Bernoulli
-
-from .. import settings
 from ..distributions import MultivariateNormal
+from ..utils.quadrature import GaussHermiteQuadrature1D
 from ..functions import log_normal_cdf, normal_cdf
 from .likelihood import Likelihood
 
@@ -21,6 +20,9 @@ class BernoulliLikelihood(Likelihood):
             p(Y=y|f)=\Phi(yf)
         \end{equation*}
     """
+    def __init__(self):
+        super().__init__()
+        self.quadrature = GaussHermiteQuadrature1D()
 
     def forward(self, input):
         if not isinstance(input, MultivariateNormal):
@@ -35,10 +37,9 @@ def forward(self, input):
         return Bernoulli(probs=output_probs)
 
     def variational_log_probability(self, latent_func, target):
-        num_samples = settings.num_likelihood_samples.value()
-        samples = latent_func.rsample(torch.Size([num_samples])).view(-1)
-        target = target.unsqueeze(0).repeat(num_samples, 1).view(-1)
-        return log_normal_cdf(samples.mul(target)).sum().div(num_samples)
+        likelihood_func = lambda locs: log_normal_cdf(locs.mul(target.unsqueeze(-1)))
+        res = self.quadrature(likelihood_func, latent_func)
+        return res.sum()
 
     def pyro_sample_y(self, variational_dist_f, y_obs, sample_shape, name_prefix=""):
         import pyro
diff --git a/gpytorch/settings.py b/gpytorch/settings.py
index 5ae1abb17..7316dae5f 100644
--- a/gpytorch/settings.py
+++ b/gpytorch/settings.py
@@ -343,6 +343,16 @@ class num_likelihood_samples(_value_context):
     _global_value = 10
 
 
+class num_gauss_hermite_locs(_value_context):
+    """
+    The number of samples to draw from a latent GP when computing a likelihood
+    This is used in variational inference and training
+    Default: 10
+    """
+
+    _global_value = 20
+
+
 class num_trace_samples(_value_context):
     """
     The number of samples to draw when stochastically computing the trace of a matrix
diff --git a/gpytorch/utils/__init__.py b/gpytorch/utils/__init__.py
index dde02c083..104e53e69 100644
--- a/gpytorch/utils/__init__.py
+++ b/gpytorch/utils/__init__.py
@@ -13,6 +13,7 @@
 from . import lanczos
 from . import pivoted_cholesky
 from . import sparse
+from . import quadrature
 
 
 def prod(items):
@@ -40,5 +41,6 @@ def prod(items):
     "interpolation",
     "lanczos",
     "pivoted_cholesky",
+    "quadrature",
     "sparse",
 ]
diff --git a/gpytorch/utils/quadrature.py b/gpytorch/utils/quadrature.py
new file mode 100644
index 000000000..a0fa7c5d5
--- /dev/null
+++ b/gpytorch/utils/quadrature.py
@@ -0,0 +1,63 @@
+#!/usr/bin/env python3
+
+import math
+import numpy as np
+import torch
+from torch.nn import Module
+from .. import settings
+
+
+class GaussHermiteQuadrature1D(Module):
+    """
+    Implements Gauss-Hermite quadrature for integrating a function with respect to several 1D Gaussian distributions
+    in batch mode. Within GPyTorch, this is useful primarily for computing expected log likelihoods for variational
+    inference.
+
+    This is implemented as a Module because Gauss-Hermite quadrature has a set of locations and weights that it
+    should initialize one time, but that should obey parent calls to .cuda(), .double() etc.
+    """
+    def __init__(self, num_locs=settings.num_gauss_hermite_locs.value()):
+        super().__init__()
+        self .num_locs = num_locs
+
+        locations, weights = self._locs_and_weights(num_locs)
+
+        self.locations = locations
+        self.weights = weights
+
+    def _apply(self, fn):
+        self.locations = fn(self.locations)
+        self.weights = fn(self.weights)
+        return super(GaussHermiteQuadrature1D, self)._apply(fn)
+
+    def _locs_and_weights(self, num_locs):
+        """
+        Get locations and weights for Gauss-Hermite quadrature. Note that this is **not** intended to be used
+        externally, because it directly creates tensors with no knowledge of a device or dtype to cast to.
+
+        Instead, create a GaussHermiteQuadrature1D object and get the locations and weights from buffers.
+        """
+        locations, weights = np.polynomial.hermite.hermgauss(num_locs)
+        locations = torch.Tensor(locations)
+        weights = torch.Tensor(weights)
+        return locations, weights
+
+    def forward(self, func, gaussian_dists):
+        """
+        Runs Gauss-Hermite quadrature on the callable func, integrating against the Gaussian distributions specified
+        by gaussian_dists.
+
+        Args:
+            - func (callable): Function to integrate
+            - gaussian_dists (Distribution): Either a MultivariateNormal whose covariance is assumed to be diagonal
+                or a :obj:`torch.distributions.Normal`.
+        Returns:
+            - Result of integrating func against each univariate Gaussian in gaussian_dists.
+        """
+        mus = gaussian_dists.mean
+        vars = gaussian_dists.variance
+
+        shifted_locs = torch.sqrt(2.0 * vars).unsqueeze(-1) * self.locations + mus.unsqueeze(-1)
+        res = (1 / math.sqrt(math.pi)) * (func(shifted_locs) * self.weights).sum(-1)
+
+        return res
diff --git a/test/utils/test_quadrature.py b/test/utils/test_quadrature.py
new file mode 100644
index 000000000..3171fafc1
--- /dev/null
+++ b/test/utils/test_quadrature.py
@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+
+import os
+import random
+import torch
+import unittest
+
+from gpytorch.utils.quadrature import GaussHermiteQuadrature1D
+from gpytorch.distributions import MultivariateNormal
+from gpytorch.lazy import DiagLazyTensor
+
+
+class TestQuadrature(unittest.TestCase):
+    def setUp(self):
+        if os.getenv("UNLOCK_SEED") is None or os.getenv("UNLOCK_SEED").lower() == "false":
+            self.rng_state = torch.get_rng_state()
+            torch.manual_seed(1)
+            if torch.cuda.is_available():
+                torch.cuda.manual_seed_all(1)
+            random.seed(1)
+
+    def tearDown(self):
+        if hasattr(self, "rng_state"):
+            torch.set_rng_state(self.rng_state)
+
+    def test_gauss_hermite_quadrature_1D_normal_nonbatch(self, cuda=False):
+        func = lambda x: torch.sin(x)
+
+        means = torch.randn(10)
+        variances = torch.randn(10).abs()
+        quadrature = GaussHermiteQuadrature1D()
+
+        if cuda:
+            means = means.cuda()
+            variances = variances.cuda()
+            quadrature = quadrature.cuda()
+
+        dist = torch.distributions.Normal(means, variances.sqrt())
+
+        # Use quadrature
+        results = quadrature(func, dist)
+
+        # Use Monte-Carlo
+        samples = dist.rsample(torch.Size([20000]))
+        actual = func(samples).mean(0)
+
+        self.assertLess(torch.mean(torch.abs(actual - results)), 0.1)
+
+    def test_gauss_hermite_quadrature_1D_normal_nonbatch_cuda(self):
+        if torch.cuda.is_available():
+            self.test_gauss_hermite_quadrature_1D_normal_nonbatch(cuda=True)
+
+    def test_gauss_hermite_quadrature_1D_normal_batch(self, cuda=False):
+        func = lambda x: torch.sin(x)
+
+        means = torch.randn(3, 10)
+        variances = torch.randn(3, 10).abs()
+        quadrature = GaussHermiteQuadrature1D()
+
+        if cuda:
+            means = means.cuda()
+            variances = variances.cuda()
+            quadrature = quadrature.cuda()
+
+        dist = torch.distributions.Normal(means, variances.sqrt())
+
+        # Use quadrature
+        results = quadrature(func, dist)
+
+        # Use Monte-Carlo
+        samples = dist.rsample(torch.Size([20000]))
+        actual = func(samples).mean(0)
+
+        self.assertLess(torch.mean(torch.abs(actual - results)), 0.1)
+
+    def test_gauss_hermite_quadrature_1D_normal_batch_cuda(self):
+        if torch.cuda.is_available():
+            self.test_gauss_hermite_quadrature_1D_normal_nonbatch(cuda=True)
+
+    def test_gauss_hermite_quadrature_1D_mvn_nonbatch(self, cuda=False):
+        func = lambda x: torch.sin(x)
+
+        means = torch.randn(10)
+        variances = torch.randn(10).abs()
+
+        quadrature = GaussHermiteQuadrature1D()
+
+        if cuda:
+            means = means.cuda()
+            variances = variances.cuda()
+            quadrature = quadrature.cuda()
+
+        dist = MultivariateNormal(means, DiagLazyTensor(variances.sqrt()))
+
+        # Use quadrature
+        results = quadrature(func, dist)
+
+        # Use Monte-Carlo
+        samples = dist.rsample(torch.Size([20000]))
+        actual = func(samples).mean(0)
+
+        self.assertLess(torch.mean(torch.abs(actual - results)), 0.1)
+
+    def test_gauss_hermite_quadrature_1D_mvn_nonbatch_cuda(self):
+        if torch.cuda.is_available():
+            self.test_gauss_hermite_quadrature_1D_normal_nonbatch(cuda=True)
+
+    def test_gauss_hermite_quadrature_1D_mvn_batch(self, cuda=False):
+        func = lambda x: torch.sin(x)
+
+        means = torch.randn(3, 10)
+        variances = torch.randn(3, 10).abs()
+        quadrature = GaussHermiteQuadrature1D()
+
+        if cuda:
+            means = means.cuda()
+            variances = variances.cuda()
+            quadrature = quadrature.cuda()
+
+        dist = MultivariateNormal(means, DiagLazyTensor(variances.sqrt()))
+
+        # Use quadrature
+        results = quadrature(func, dist)
+
+        # Use Monte-Carlo
+        samples = dist.rsample(torch.Size([20000]))
+        actual = func(samples).mean(0)
+
+        self.assertLess(torch.mean(torch.abs(actual - results)), 0.1)
+
+    def test_gauss_hermite_quadrature_1D_mvn_batch_cuda(self):
+        if torch.cuda.is_available():
+            self.test_gauss_hermite_quadrature_1D_normal_nonbatch(cuda=True)
+
+
+if __name__ == "__main__":
+    unittest.main()

From 195fa8b1044423fa6b399e19a99375c60ae9960f Mon Sep 17 00:00:00 2001
From: Alex Wang <kaw293@cornell.edu>
Date: Sat, 26 Jan 2019 12:03:18 -0500
Subject: [PATCH 21/25] remove log_ kwargs deprecation addresses comments from
 #478

---
 gpytorch/kernels/cosine_kernel.py             |  4 --
 gpytorch/kernels/kernel.py                    |  2 -
 gpytorch/kernels/matern_kernel.py             |  2 -
 gpytorch/kernels/periodic_kernel.py           |  5 --
 gpytorch/kernels/rbf_kernel.py                |  2 -
 gpytorch/kernels/scale_kernel.py              |  2 -
 gpytorch/kernels/spectral_mixture_kernel.py   | 11 ----
 gpytorch/likelihoods/gaussian_likelihood.py   |  2 -
 .../multitask_gaussian_likelihood.py          |  5 --
 gpytorch/module.py                            | 60 ++-----------------
 gpytorch/utils/log_deprecation.py             | 31 ----------
 11 files changed, 4 insertions(+), 122 deletions(-)
 delete mode 100644 gpytorch/utils/log_deprecation.py

diff --git a/gpytorch/kernels/cosine_kernel.py b/gpytorch/kernels/cosine_kernel.py
index a84b45e41..5c756e3dd 100644
--- a/gpytorch/kernels/cosine_kernel.py
+++ b/gpytorch/kernels/cosine_kernel.py
@@ -3,7 +3,6 @@
 import math
 import torch
 from .kernel import Kernel
-from ..utils.deprecation import _deprecate_kwarg
 from torch.nn.functional import softplus
 
 
@@ -66,9 +65,6 @@ def __init__(
         inv_param_transform=None,
         **kwargs
     ):
-        period_length_prior = _deprecate_kwarg(
-            kwargs, "log_period_length_prior", "period_length_prior", period_length_prior
-        )
         super(CosineKernel, self).__init__(
             active_dims=active_dims, param_transform=param_transform, inv_param_transform=inv_param_transform
         )
diff --git a/gpytorch/kernels/kernel.py b/gpytorch/kernels/kernel.py
index 2fa2cecb6..b03cf3c5e 100644
--- a/gpytorch/kernels/kernel.py
+++ b/gpytorch/kernels/kernel.py
@@ -6,7 +6,6 @@
 from ..lazy import lazify, LazyEvaluatedKernelTensor, ZeroLazyTensor
 from ..module import Module
 from .. import settings
-from ..utils.deprecation import _deprecate_kwarg
 from ..utils.transforms import _get_inv_param_transform
 from torch.nn.functional import softplus
 from numpy import triu_indices
@@ -138,7 +137,6 @@ def __init__(
         eps=1e-6,
         **kwargs
     ):
-        lengthscale_prior = _deprecate_kwarg(kwargs, "log_lengthscale_prior", "lengthscale_prior", lengthscale_prior)
         super(Kernel, self).__init__()
         if active_dims is not None and not torch.is_tensor(active_dims):
             active_dims = torch.tensor(active_dims, dtype=torch.long)
diff --git a/gpytorch/kernels/matern_kernel.py b/gpytorch/kernels/matern_kernel.py
index 5e2136399..5925c4ae2 100644
--- a/gpytorch/kernels/matern_kernel.py
+++ b/gpytorch/kernels/matern_kernel.py
@@ -3,7 +3,6 @@
 import math
 import torch
 from .kernel import Kernel
-from ..utils.deprecation import _deprecate_kwarg
 from torch.nn.functional import softplus
 
 
@@ -92,7 +91,6 @@ def __init__(
         eps=1e-6,
         **kwargs
     ):
-        _deprecate_kwarg(kwargs, "log_lengthscale_prior", "lengthscale_prior", lengthscale_prior)
         if nu not in {0.5, 1.5, 2.5}:
             raise RuntimeError("nu expected to be 0.5, 1.5, or 2.5")
         super(MaternKernel, self).__init__(
diff --git a/gpytorch/kernels/periodic_kernel.py b/gpytorch/kernels/periodic_kernel.py
index 6bbf6c6ce..723d8ea96 100644
--- a/gpytorch/kernels/periodic_kernel.py
+++ b/gpytorch/kernels/periodic_kernel.py
@@ -3,7 +3,6 @@
 import math
 import torch
 from .kernel import Kernel
-from ..utils.deprecation import _deprecate_kwarg
 from torch.nn.functional import softplus
 
 
@@ -83,10 +82,6 @@ def __init__(
         eps=1e-6,
         **kwargs
     ):
-        lengthscale_prior = _deprecate_kwarg(kwargs, "log_lengthscale_prior", "lengthscale_prior", lengthscale_prior)
-        period_length_prior = _deprecate_kwarg(
-            kwargs, "log_period_length_prior", "period_length_prior", period_length_prior
-        )
         super(PeriodicKernel, self).__init__(
             has_lengthscale=True,
             active_dims=active_dims,
diff --git a/gpytorch/kernels/rbf_kernel.py b/gpytorch/kernels/rbf_kernel.py
index d0b35fd24..8de542cb4 100644
--- a/gpytorch/kernels/rbf_kernel.py
+++ b/gpytorch/kernels/rbf_kernel.py
@@ -1,7 +1,6 @@
 #!/usr/bin/env python3
 
 from .kernel import Kernel
-from ..utils.deprecation import _deprecate_kwarg
 from torch.nn.functional import softplus
 import torch
 
@@ -83,7 +82,6 @@ def __init__(
         eps=1e-6,
         **kwargs
     ):
-        _deprecate_kwarg(kwargs, "log_lengthscale_prior", "lengthscale_prior", lengthscale_prior)
         super(RBFKernel, self).__init__(
             has_lengthscale=True,
             ard_num_dims=ard_num_dims,
diff --git a/gpytorch/kernels/scale_kernel.py b/gpytorch/kernels/scale_kernel.py
index b6a4cc4d0..618380d73 100644
--- a/gpytorch/kernels/scale_kernel.py
+++ b/gpytorch/kernels/scale_kernel.py
@@ -2,7 +2,6 @@
 
 import torch
 from .kernel import Kernel
-from ..utils.deprecation import _deprecate_kwarg
 from ..utils.transforms import _get_inv_param_transform
 from torch.nn.functional import softplus
 from ..lazy import delazify
@@ -64,7 +63,6 @@ def __init__(
         inv_param_transform=None,
         **kwargs
     ):
-        outputscale_prior = _deprecate_kwarg(kwargs, "log_outputscale_prior", "outputscale_prior", outputscale_prior)
         super(ScaleKernel, self).__init__(has_lengthscale=False, batch_size=batch_size)
         self.base_kernel = base_kernel
         self._param_transform = param_transform
diff --git a/gpytorch/kernels/spectral_mixture_kernel.py b/gpytorch/kernels/spectral_mixture_kernel.py
index c8e6cbf33..3d97cdf13 100644
--- a/gpytorch/kernels/spectral_mixture_kernel.py
+++ b/gpytorch/kernels/spectral_mixture_kernel.py
@@ -4,7 +4,6 @@
 import math
 import torch
 from .kernel import Kernel
-from ..utils.deprecation import _deprecate_kwarg
 from torch.nn.functional import softplus
 
 logger = logging.getLogger()
@@ -83,16 +82,6 @@ def __init__(
         inv_param_transform=None,
         **kwargs
     ):
-        mixture_scales_prior = _deprecate_kwarg(
-            kwargs, "log_mixture_scales_prior", "mixture_scales_prior", mixture_scales_prior
-        )
-        mixture_means_prior = _deprecate_kwarg(
-            kwargs, "log_mixture_means_prior", "mixture_means_prior", mixture_means_prior
-        )
-        mixture_weights_prior = _deprecate_kwarg(
-            kwargs, "log_mixture_weights_prior", "mixture_weights_prior", mixture_weights_prior
-        )
-
         if num_mixtures is None:
             raise RuntimeError("num_mixtures is a required argument")
         if mixture_means_prior is not None or mixture_scales_prior is not None or mixture_weights_prior is not None:
diff --git a/gpytorch/likelihoods/gaussian_likelihood.py b/gpytorch/likelihoods/gaussian_likelihood.py
index 0175aff16..9c7054047 100644
--- a/gpytorch/likelihoods/gaussian_likelihood.py
+++ b/gpytorch/likelihoods/gaussian_likelihood.py
@@ -8,7 +8,6 @@
 from ..distributions import MultivariateNormal
 from ..likelihoods import Likelihood
 from ..lazy import BlockDiagLazyTensor, DiagLazyTensor
-from ..utils.deprecation import _deprecate_kwarg
 from .noise_models import HomoskedasticNoise
 
 
@@ -39,7 +38,6 @@ def variational_log_probability(self, input, target):
 
 class GaussianLikelihood(_GaussianLikelihoodBase):
     def __init__(self, noise_prior=None, batch_size=1, param_transform=softplus, inv_param_transform=None, **kwargs):
-        noise_prior = _deprecate_kwarg(kwargs, "log_noise_prior", "noise_prior", noise_prior)
         noise_covar = HomoskedasticNoise(
             noise_prior=noise_prior,
             batch_size=batch_size,
diff --git a/gpytorch/likelihoods/multitask_gaussian_likelihood.py b/gpytorch/likelihoods/multitask_gaussian_likelihood.py
index c540f662e..c16406b9e 100644
--- a/gpytorch/likelihoods/multitask_gaussian_likelihood.py
+++ b/gpytorch/likelihoods/multitask_gaussian_likelihood.py
@@ -14,7 +14,6 @@
     RootLazyTensor,
 )
 from ..likelihoods import Likelihood, _GaussianLikelihoodBase
-from ..utils.deprecation import _deprecate_kwarg
 from ..utils.transforms import _get_inv_param_transform
 from .noise_models import MultitaskHomoskedasticNoise
 
@@ -149,9 +148,6 @@ def __init__(
             Only used when `rank` > 0.
 
         """
-        task_correlation_prior = _deprecate_kwarg(
-            kwargs, "task_prior", "task_correlation_prior", task_correlation_prior
-        )
         noise_covar = MultitaskHomoskedasticNoise(
             num_tasks=num_tasks,
             noise_prior=noise_prior,
@@ -230,7 +226,6 @@ def __init__(
             `rank` > 0, or a prior over the log of just the diagonal elements, if `rank` == 0.
 
         """
-        noise_prior = _deprecate_kwarg(kwargs, "log_noise_prior", "noise_prior", noise_prior)
         super(Likelihood, self).__init__()
         self._param_transform = param_transform
         self._inv_param_transform = _get_inv_param_transform(param_transform, inv_param_transform)
diff --git a/gpytorch/module.py b/gpytorch/module.py
index 7b5803594..2865a1568 100644
--- a/gpytorch/module.py
+++ b/gpytorch/module.py
@@ -63,26 +63,16 @@ def hyperparameters(self):
             yield param
 
     def initialize(self, **kwargs):
-        # TODO: Change to initialize actual parameter (e.g. lengthscale) rather than untransformed parameter.
         """
         Set a value for a parameter
 
         kwargs: (param_name, value) - parameter to initialize
         Value can take the form of a tensor, a float, or an int
         """
-        from .utils.log_deprecation import MODULES_WITH_LOG_PARAMS
 
         for name, val in kwargs.items():
             if isinstance(val, int):
                 val = float(val)
-            if any(isinstance(self, mod_type) for mod_type in MODULES_WITH_LOG_PARAMS) and "log_" in name:
-                base_name = name.split("log_")[1]
-                name = "raw_" + base_name
-                if not torch.is_tensor(val):
-                    val = self._inv_param_transform(torch.tensor(val).exp()).item()
-                else:
-                    val = self._inv_param_transform(val.exp())
-
             if not hasattr(self, name):
                 raise AttributeError("Unknown parameter {p} for {c}".format(p=name, c=self.__class__.__name__))
             elif name not in self._parameters:
@@ -234,56 +224,14 @@ def variational_parameters(self):
         for _, param in self.named_variational_parameters():
             yield param
 
-    def _load_from_state_dict(
-        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-    ):
-        from .utils.log_deprecation import LOG_DEPRECATION_MSG, MODULES_WITH_LOG_PARAMS
-
-        local_name_params = itertools.chain(self._parameters.items(), self._buffers.items())
-        local_state = {k: v.data for k, v in local_name_params if v is not None}
-
-        super()._load_from_state_dict(
-            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-        )
-        if not any(isinstance(self, mod_type) for mod_type in MODULES_WITH_LOG_PARAMS):
-            return
-
-        # Load log space parameters and throw deprecation warnings.
-        for name, param in local_state.items():
-            if "raw_" in name:
-                base_name = name.split("raw_")[1]
-                log_name = "log_" + base_name
-                log_key = prefix + log_name
-                if log_key in state_dict and log_key not in local_state:
-                    warnings.warn(LOG_DEPRECATION_MSG.format(log_name=log_name, name=name), DeprecationWarning)
-                    input_param = state_dict[log_key]
-                    if isinstance(input_param, nn.Parameter):
-                        input_param = input_param.data
-
-                    real_input_param = self._inv_param_transform(input_param.exp())
-                    param.copy_(real_input_param)
-
-                    if prefix + name in missing_keys:
-                        missing_keys.remove(prefix + name)
-                    if prefix + name in unexpected_keys:
-                        unexpected_keys.remove(prefix + log_name)
-
     def __getattr__(self, name):
         try:
             return super().__getattr__(name)
         except AttributeError as e:
-            from .utils.log_deprecation import LOG_DEPRECATION_MSG, MODULES_WITH_LOG_PARAMS
-
-            if any(isinstance(self, mod_type) for mod_type in MODULES_WITH_LOG_PARAMS) and "log_" in name:
-                base_name = name.split("log_")[1]  # e.g. log_lengthscale -> lengthscale
-                raw_name = "raw_" + base_name
-                warnings.warn(LOG_DEPRECATION_MSG.format(log_name=name, name=raw_name), DeprecationWarning)
-                return super().__getattribute__(base_name).log()  # Get real param value and transform to log
-            else:
-                try:
-                    return super().__getattribute__(name)
-                except AttributeError:
-                    raise e
+            try:
+                return super().__getattribute__(name)
+            except AttributeError:
+                raise e
 
 
 def _extract_named_added_loss_terms(module, memo=None, prefix=""):
diff --git a/gpytorch/utils/log_deprecation.py b/gpytorch/utils/log_deprecation.py
deleted file mode 100644
index b7f786f89..000000000
--- a/gpytorch/utils/log_deprecation.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env python3
-
-from ..kernels import (
-    CosineKernel,
-    IndexKernel,
-    MaternKernel,
-    PeriodicKernel,
-    RBFKernel,
-    ScaleKernel,
-    SpectralMixtureKernel,
-)
-from ..likelihoods import GaussianLikelihood, MultitaskGaussianLikelihood
-
-
-MODULES_WITH_LOG_PARAMS = [
-    CosineKernel,
-    IndexKernel,
-    MaternKernel,
-    PeriodicKernel,
-    RBFKernel,
-    ScaleKernel,
-    SpectralMixtureKernel,
-    GaussianLikelihood,
-    MultitaskGaussianLikelihood,
-]
-
-LOG_DEPRECATION_MSG = (
-    "The '{log_name}' parameter is deprecated in favor of '{name}'  because we no longer ensure "
-    "positiveness with torch.exp for improved stability reasons and will be removed in a future "
-    "release."
-)

From 4108f3b53364411530cb26726a262264343fd181 Mon Sep 17 00:00:00 2001
From: Alex Wang <kaw293@cornell.edu>
Date: Sat, 26 Jan 2019 12:23:24 -0500
Subject: [PATCH 22/25] fix GaussianLikelihood initialize noise bug closes #478

---
 gpytorch/likelihoods/gaussian_likelihood.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gpytorch/likelihoods/gaussian_likelihood.py b/gpytorch/likelihoods/gaussian_likelihood.py
index 9c7054047..71926e1c1 100644
--- a/gpytorch/likelihoods/gaussian_likelihood.py
+++ b/gpytorch/likelihoods/gaussian_likelihood.py
@@ -58,7 +58,7 @@ def noise(self):
 
     @noise.setter
     def noise(self, value):
-        self.noise_covar.initialize(value)
+        self.noise_covar.initialize(noise=value)
 
     @property
     def raw_noise(self):

From fc51facd0c7bc9f5b232678232b89f15b920e40f Mon Sep 17 00:00:00 2001
From: Alex Wang <kaw293@cornell.edu>
Date: Sat, 26 Jan 2019 13:31:51 -0500
Subject: [PATCH 23/25] update test files to not use log parameters

---
 gpytorch/module.py                           |  2 --
 test/examples/test_simple_gp_regression.py   | 26 ++++++++---------
 test/examples/test_white_noise_regression.py | 14 ++++-----
 test/kernels/test_additive_kernel.py         | 30 ++++++++++----------
 test/kernels/test_cosine_kernel.py           |  6 ++--
 test/kernels/test_matern_kernel.py           | 12 ++++----
 test/kernels/test_periodic_kernel.py         |  6 ++--
 test/kernels/test_rbf_kernel.py              | 14 ++++-----
 test/kernels/test_scale_kernel.py            |  8 +++---
 test/kernels/test_spectral_mixture_kernel.py |  8 +++---
 10 files changed, 62 insertions(+), 64 deletions(-)

diff --git a/gpytorch/module.py b/gpytorch/module.py
index 2865a1568..805c97485 100644
--- a/gpytorch/module.py
+++ b/gpytorch/module.py
@@ -1,7 +1,5 @@
 #!/usr/bin/env python3
 
-import itertools
-import warnings
 from collections import OrderedDict
 
 import torch
diff --git a/test/examples/test_simple_gp_regression.py b/test/examples/test_simple_gp_regression.py
index 912e3a9d1..d39ab944a 100644
--- a/test/examples/test_simple_gp_regression.py
+++ b/test/examples/test_simple_gp_regression.py
@@ -57,11 +57,11 @@ def test_prior(self, cuda=False):
         gp_model = ExactGPModel(None, None, likelihood)
         # Update lengthscale prior to accommodate extreme parameters
         gp_model.covar_module.base_kernel.register_prior(
-            "log_lengthscale_prior", SmoothedBoxPrior(exp(-10), exp(10), sigma=0.5), "raw_lengthscale"
+            "lengthscale_prior", SmoothedBoxPrior(exp(-10), exp(10), sigma=0.5), "raw_lengthscale"
         )
         gp_model.mean_module.initialize(constant=1.5)
-        gp_model.covar_module.base_kernel.initialize(log_lengthscale=0)
-        likelihood.initialize(log_noise=0)
+        gp_model.covar_module.base_kernel.initialize(lengthscale=1)
+        likelihood.initialize(noise=0)
 
         if cuda:
             gp_model.cuda()
@@ -87,8 +87,8 @@ def test_posterior_latent_gp_and_likelihood_without_optimization(self, cuda=Fals
         # We're manually going to set the hyperparameters to be ridiculous
         likelihood = GaussianLikelihood()
         gp_model = ExactGPModel(train_x, train_y, likelihood)
-        gp_model.covar_module.base_kernel.initialize(raw_lengthscale=-15)
-        likelihood.initialize(log_noise=-15)
+        gp_model.covar_module.base_kernel.initialize(lengthscale=exp(-15))
+        likelihood.initialize(noise=exp(-15))
 
         if cuda:
             gp_model.cuda()
@@ -122,9 +122,9 @@ def test_posterior_latent_gp_and_likelihood_with_optimization(self, cuda=False):
         likelihood = GaussianLikelihood(noise_prior=SmoothedBoxPrior(exp(-3), exp(3), sigma=0.1))
         gp_model = ExactGPModel(train_x, train_y, likelihood)
         mll = gpytorch.ExactMarginalLogLikelihood(likelihood, gp_model)
-        gp_model.covar_module.base_kernel.initialize(log_lengthscale=1)
+        gp_model.covar_module.base_kernel.initialize(lengthscale=exp(1))
         gp_model.mean_module.initialize(constant=0)
-        likelihood.initialize(log_noise=1)
+        likelihood.initialize(noise=exp(1))
 
         if cuda:
             gp_model.cuda()
@@ -170,9 +170,9 @@ def test_fantasy_updates(self, cuda=False):
         likelihood = GaussianLikelihood()
         gp_model = ExactGPModel(train_x, train_y, likelihood)
         mll = gpytorch.ExactMarginalLogLikelihood(likelihood, gp_model)
-        gp_model.covar_module.base_kernel.initialize(log_lengthscale=1)
+        gp_model.covar_module.base_kernel.initialize(lengthscale=exp(1))
         gp_model.mean_module.initialize(constant=0)
-        likelihood.initialize(log_noise=1)
+        likelihood.initialize(noise=exp(1))
 
         if cuda:
             gp_model.cuda()
@@ -240,9 +240,9 @@ def test_fantasy_updates_batch(self, cuda=False):
         likelihood = GaussianLikelihood()
         gp_model = ExactGPModel(train_x, train_y, likelihood)
         mll = gpytorch.ExactMarginalLogLikelihood(likelihood, gp_model)
-        gp_model.covar_module.base_kernel.initialize(log_lengthscale=1)
+        gp_model.covar_module.base_kernel.initialize(lengthscale=exp(1))
         gp_model.mean_module.initialize(constant=0)
-        likelihood.initialize(log_noise=1)
+        likelihood.initialize(noise=exp(1))
 
         if cuda:
             gp_model.cuda()
@@ -311,9 +311,9 @@ def test_posterior_latent_gp_and_likelihood_fast_pred_var(self, cuda=False):
             likelihood = GaussianLikelihood(noise_prior=SmoothedBoxPrior(exp(-3), exp(3), sigma=0.1))
             gp_model = ExactGPModel(train_x, train_y, likelihood)
             mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, gp_model)
-            gp_model.covar_module.base_kernel.initialize(log_lengthscale=1)
+            gp_model.covar_module.base_kernel.initialize(lengthscale=exp(1))
             gp_model.mean_module.initialize(constant=0)
-            likelihood.initialize(log_noise=1)
+            likelihood.initialize(noise=exp(1))
 
             if cuda:
                 gp_model.cuda()
diff --git a/test/examples/test_white_noise_regression.py b/test/examples/test_white_noise_regression.py
index 5a256252a..bca9c0f7b 100644
--- a/test/examples/test_white_noise_regression.py
+++ b/test/examples/test_white_noise_regression.py
@@ -59,11 +59,11 @@ def test_posterior_latent_gp_and_likelihood_without_optimization(self, cuda=Fals
             gp_model = ExactGPModel(train_x, train_y, likelihood)
             # Update lengthscale prior to accommodate extreme parameters
             gp_model.rbf_covar_module.register_prior(
-                "log_lengthscale_prior", SmoothedBoxPrior(exp(-10), exp(10), sigma=0.5), "raw_lengthscale"
+                "lengthscale_prior", SmoothedBoxPrior(exp(-10), exp(10), sigma=0.5), "raw_lengthscale"
             )
-            gp_model.rbf_covar_module.initialize(log_lengthscale=-10)
+            gp_model.rbf_covar_module.initialize(lengthscale=exp(-10))
             gp_model.mean_module.initialize(constant=0)
-            likelihood.initialize(log_noise=-10)
+            likelihood.initialize(noise=exp(-10))
 
             if cuda:
                 gp_model.cuda()
@@ -96,9 +96,9 @@ def test_posterior_latent_gp_and_likelihood_with_optimization(self, cuda=False):
         likelihood = GaussianLikelihood(noise_prior=SmoothedBoxPrior(exp(-3), exp(3), sigma=0.1))
         gp_model = ExactGPModel(train_x, train_y, likelihood)
         mll = gpytorch.ExactMarginalLogLikelihood(likelihood, gp_model)
-        gp_model.rbf_covar_module.initialize(log_lengthscale=1)
+        gp_model.rbf_covar_module.initialize(lengthscale=exp(1))
         gp_model.mean_module.initialize(constant=0)
-        likelihood.initialize(log_noise=1)
+        likelihood.initialize(noise=exp(1))
 
         if cuda:
             gp_model.cuda()
@@ -146,9 +146,9 @@ def test_posterior_latent_gp_and_likelihood_fast_pred_var(self, cuda=False):
             likelihood = GaussianLikelihood(noise_prior=SmoothedBoxPrior(exp(-3), exp(3), sigma=0.1))
             gp_model = ExactGPModel(train_x, train_y, likelihood)
             mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, gp_model)
-            gp_model.rbf_covar_module.initialize(log_lengthscale=1)
+            gp_model.rbf_covar_module.initialize(lengthscale=exp(1))
             gp_model.mean_module.initialize(constant=0)
-            likelihood.initialize(log_noise=1)
+            likelihood.initialize(noise=exp(1))
 
             if cuda:
                 gp_model.cuda()
diff --git a/test/kernels/test_additive_kernel.py b/test/kernels/test_additive_kernel.py
index d0727d20b..301ac94c5 100644
--- a/test/kernels/test_additive_kernel.py
+++ b/test/kernels/test_additive_kernel.py
@@ -12,8 +12,8 @@ def test_computes_product_of_radial_basis_function(self):
         b = torch.tensor([0, 2], dtype=torch.float).view(2, 1)
         lengthscale = 2
 
-        kernel_1 = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
-        kernel_2 = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
+        kernel_1 = RBFKernel().initialize(lengthscale=lengthscale)
+        kernel_2 = RBFKernel().initialize(lengthscale=lengthscale)
         kernel = kernel_1 * kernel_2
 
         actual = torch.tensor([[16, 4], [4, 0], [64, 36]], dtype=torch.float)
@@ -28,8 +28,8 @@ def test_computes_sum_of_radial_basis_function(self):
         b = torch.tensor([0, 2], dtype=torch.float).view(2, 1)
         lengthscale = 2
 
-        kernel_1 = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
-        kernel_2 = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
+        kernel_1 = RBFKernel().initialize(lengthscale=lengthscale)
+        kernel_2 = RBFKernel().initialize(lengthscale=lengthscale)
         kernel = kernel_1 + kernel_2
 
         actual = torch.tensor([[16, 4], [4, 0], [64, 36]], dtype=torch.float)
@@ -44,9 +44,9 @@ def test_computes_product_of_three_radial_basis_function(self):
         b = torch.tensor([0, 2], dtype=torch.float).view(2, 1)
         lengthscale = 2
 
-        kernel_1 = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
-        kernel_2 = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
-        kernel_3 = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
+        kernel_1 = RBFKernel().initialize(lengthscale=lengthscale)
+        kernel_2 = RBFKernel().initialize(lengthscale=lengthscale)
+        kernel_3 = RBFKernel().initialize(lengthscale=lengthscale)
         kernel = ProductKernel(kernel_1, kernel_2, kernel_3)
 
         actual = torch.tensor([[16, 4], [4, 0], [64, 36]], dtype=torch.float)
@@ -61,9 +61,9 @@ def test_computes_sum_of_three_radial_basis_function(self):
         b = torch.tensor([0, 2], dtype=torch.float).view(2, 1)
         lengthscale = 2
 
-        kernel_1 = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
-        kernel_2 = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
-        kernel_3 = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
+        kernel_1 = RBFKernel().initialize(lengthscale=lengthscale)
+        kernel_2 = RBFKernel().initialize(lengthscale=lengthscale)
+        kernel_3 = RBFKernel().initialize(lengthscale=lengthscale)
         kernel = AdditiveKernel(kernel_1, kernel_2, kernel_3)
 
         actual = (
@@ -87,8 +87,8 @@ def test_computes_sum_radial_basis_function_gradient(self):
         actual_output.backward(torch.eye(3))
         actual_param_grad = param.grad.sum() * 2
 
-        kernel_1 = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
-        kernel_2 = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
+        kernel_1 = RBFKernel().initialize(lengthscale=lengthscale)
+        kernel_2 = RBFKernel().initialize(lengthscale=lengthscale)
         kernel = kernel_1 + kernel_2
         kernel.eval()
 
@@ -110,9 +110,9 @@ def test_computes_sum_three_radial_basis_function_gradient(self):
         actual_output.backward(torch.eye(3))
         actual_param_grad = param.grad.sum() * 3
 
-        kernel_1 = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
-        kernel_2 = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
-        kernel_3 = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
+        kernel_1 = RBFKernel().initialize(lengthscale=lengthscale)
+        kernel_2 = RBFKernel().initialize(lengthscale=lengthscale)
+        kernel_3 = RBFKernel().initialize(lengthscale=lengthscale)
         kernel = AdditiveKernel(kernel_1, kernel_2, kernel_3)
         kernel.eval()
 
diff --git a/test/kernels/test_cosine_kernel.py b/test/kernels/test_cosine_kernel.py
index d2efa74e2..e422ddab0 100644
--- a/test/kernels/test_cosine_kernel.py
+++ b/test/kernels/test_cosine_kernel.py
@@ -11,7 +11,7 @@ def test_computes_periodic_function(self):
         a = torch.tensor([[4, 1], [2, 2], [8, 0]], dtype=torch.float)
         b = torch.tensor([[0, 0], [2, 1], [1, 0]], dtype=torch.float)
         period = 1
-        kernel = CosineKernel().initialize(log_period_length=math.log(period))
+        kernel = CosineKernel().initialize(period_length=period)
         kernel.eval()
 
         actual = torch.zeros(3, 3)
@@ -45,7 +45,7 @@ def test_batch(self):
         a = torch.tensor([[4, 2, 8], [1, 2, 3]], dtype=torch.float).view(2, 3, 1)
         b = torch.tensor([[0, 2, 1], [-1, 2, 0]], dtype=torch.float).view(2, 3, 1)
         period = torch.tensor(1, dtype=torch.float).view(1, 1, 1)
-        kernel = CosineKernel().initialize(log_period_length=torch.log(period))
+        kernel = CosineKernel().initialize(period_length=period)
         kernel.eval()
 
         actual = torch.zeros(2, 3, 3)
@@ -61,7 +61,7 @@ def test_batch_separate(self):
         a = torch.tensor([[[4, 1], [2, 2], [8, 0]], [[2, 5], [6, 1], [0, 1]]], dtype=torch.float)
         b = torch.tensor([[[0, 0], [2, 1], [1, 0]], [[1, 1], [2, 3], [1, 0]]], dtype=torch.float)
         period = torch.tensor([1, 2], dtype=torch.float).view(2, 1, 1)
-        kernel = CosineKernel(batch_size=2).initialize(log_period_length=torch.log(period))
+        kernel = CosineKernel(batch_size=2).initialize(period_length=period)
         kernel.eval()
 
         actual = torch.zeros(2, 3, 3)
diff --git a/test/kernels/test_matern_kernel.py b/test/kernels/test_matern_kernel.py
index 8dbc11dff..fe1b0bea6 100644
--- a/test/kernels/test_matern_kernel.py
+++ b/test/kernels/test_matern_kernel.py
@@ -12,7 +12,7 @@ def test_forward_nu_1_over_2(self):
         b = torch.tensor([0, 2], dtype=torch.float).view(2, 1)
         lengthscale = 2
 
-        kernel = MaternKernel(nu=0.5).initialize(log_lengthscale=math.log(lengthscale))
+        kernel = MaternKernel(nu=0.5).initialize(lengthscale=lengthscale)
         kernel.eval()
 
         actual = torch.tensor([[4, 2], [2, 0], [8, 6]], dtype=torch.float).div_(-lengthscale).exp()
@@ -24,7 +24,7 @@ def test_forward_nu_3_over_2(self):
         b = torch.tensor([0, 2], dtype=torch.float).view(2, 1)
         lengthscale = 2
 
-        kernel = MaternKernel(nu=1.5).initialize(log_lengthscale=math.log(lengthscale))
+        kernel = MaternKernel(nu=1.5).initialize(lengthscale=lengthscale)
         kernel.eval()
 
         dist = torch.tensor([[4, 2], [2, 0], [8, 6]], dtype=torch.float).mul_(math.sqrt(3) / lengthscale)
@@ -37,7 +37,7 @@ def test_forward_nu_5_over_2(self):
         b = torch.tensor([0, 2], dtype=torch.float).view(2, 1)
         lengthscale = 2
 
-        kernel = MaternKernel(nu=2.5).initialize(log_lengthscale=math.log(lengthscale))
+        kernel = MaternKernel(nu=2.5).initialize(lengthscale=lengthscale)
         kernel.eval()
 
         dist = torch.tensor([[4, 2], [2, 0], [8, 6]], dtype=torch.float).mul_(math.sqrt(5) / lengthscale)
@@ -51,7 +51,7 @@ def test_ard(self):
         lengthscales = torch.tensor([1, 2], dtype=torch.float).view(1, 1, 2)
 
         kernel = MaternKernel(nu=2.5, ard_num_dims=2)
-        kernel.initialize(log_lengthscale=torch.log(lengthscales))
+        kernel.initialize(lengthscale=lengthscales)
         kernel.eval()
 
         dist = torch.tensor([[1, 1], [2, 2]], dtype=torch.float)
@@ -83,7 +83,7 @@ def test_ard_batch(self):
         lengthscales = torch.tensor([[[1, 2, 1]]], dtype=torch.float)
 
         kernel = MaternKernel(nu=2.5, batch_size=2, ard_num_dims=3)
-        kernel.initialize(log_lengthscale=torch.log(lengthscales))
+        kernel.initialize(lengthscale=lengthscales)
         kernel.eval()
 
         dist = torch.tensor([[[1], [1]], [[2], [0]]], dtype=torch.float).mul_(math.sqrt(5))
@@ -97,7 +97,7 @@ def test_ard_separate_batch(self):
         lengthscales = torch.tensor([[[1, 2, 1]], [[2, 1, 0.5]]], dtype=torch.float)
 
         kernel = MaternKernel(nu=2.5, batch_size=2, ard_num_dims=3)
-        kernel.initialize(log_lengthscale=torch.log(lengthscales))
+        kernel.initialize(lengthscale=lengthscales)
         kernel.eval()
 
         dist = torch.tensor([[[1, 1], [1, 1]], [[4, 4], [0, 0]]], dtype=torch.float).mul_(math.sqrt(5))
diff --git a/test/kernels/test_periodic_kernel.py b/test/kernels/test_periodic_kernel.py
index 4646e1c04..00733a6a9 100644
--- a/test/kernels/test_periodic_kernel.py
+++ b/test/kernels/test_periodic_kernel.py
@@ -12,7 +12,7 @@ def test_computes_periodic_function(self):
         b = torch.tensor([0, 2], dtype=torch.float).view(2, 1)
         lengthscale = 2
         period = 3
-        kernel = PeriodicKernel().initialize(log_lengthscale=math.log(lengthscale), log_period_length=math.log(period))
+        kernel = PeriodicKernel().initialize(lengthscale=lengthscale, period_length=period)
         kernel.eval()
 
         actual = torch.zeros(3, 2)
@@ -30,7 +30,7 @@ def test_batch(self):
         period = torch.tensor(1, dtype=torch.float).view(1, 1, 1)
         lengthscale = torch.tensor(2, dtype=torch.float).view(1, 1, 1)
         kernel = PeriodicKernel().initialize(
-            log_lengthscale=torch.log(lengthscale), log_period_length=torch.log(period)
+            lengthscale=lengthscale, period_length=period
         )
         kernel.eval()
 
@@ -50,7 +50,7 @@ def test_batch_separate(self):
         period = torch.tensor([1, 2], dtype=torch.float).view(2, 1, 1)
         lengthscale = torch.tensor([2, 1], dtype=torch.float).view(2, 1, 1)
         kernel = PeriodicKernel(batch_size=2).initialize(
-            log_lengthscale=torch.log(lengthscale), log_period_length=torch.log(period)
+            lengthscale=lengthscale, period_length=period
         )
         kernel.eval()
 
diff --git a/test/kernels/test_rbf_kernel.py b/test/kernels/test_rbf_kernel.py
index 740fadf52..3fd150fce 100644
--- a/test/kernels/test_rbf_kernel.py
+++ b/test/kernels/test_rbf_kernel.py
@@ -13,7 +13,7 @@ def test_ard(self):
         lengthscales = torch.tensor([1, 2], dtype=torch.float).view(1, 1, 2)
 
         kernel = RBFKernel(ard_num_dims=2)
-        kernel.initialize(log_lengthscale=lengthscales.log())
+        kernel.initialize(lengthscale=lengthscales)
         kernel.eval()
 
         scaled_a = a.div(lengthscales)
@@ -44,7 +44,7 @@ def test_ard_batch(self):
         lengthscales = torch.tensor([[[1, 2, 1]]], dtype=torch.float)
 
         kernel = RBFKernel(batch_size=2, ard_num_dims=3)
-        kernel.initialize(log_lengthscale=lengthscales.log())
+        kernel.initialize(lengthscale=lengthscales)
         kernel.eval()
 
         scaled_a = a.div(lengthscales)
@@ -75,7 +75,7 @@ def test_ard_separate_batch(self):
         lengthscales = torch.tensor([[[1, 2, 1]], [[2, 1, 0.5]]], dtype=torch.float)
 
         kernel = RBFKernel(batch_size=2, ard_num_dims=3)
-        kernel.initialize(log_lengthscale=lengthscales.log())
+        kernel.initialize(lengthscale=lengthscales)
         kernel.eval()
 
         scaled_a = a.div(lengthscales)
@@ -97,7 +97,7 @@ def test_subset_active_compute_radial_basis_function(self):
         lengthscale = 2
 
         kernel = RBFKernel(active_dims=[0])
-        kernel.initialize(log_lengthscale=math.log(lengthscale))
+        kernel.initialize(lengthscale=lengthscale)
         kernel.eval()
 
         actual = torch.tensor([[16, 4, 0], [4, 0, 4], [64, 36, 16]], dtype=torch.float)
@@ -115,7 +115,7 @@ def test_computes_radial_basis_function(self):
         b = torch.tensor([0, 2, 4], dtype=torch.float).view(3, 1)
         lengthscale = 2
 
-        kernel = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
+        kernel = RBFKernel().initialize(lengthscale=lengthscale)
         kernel.eval()
 
         actual = torch.tensor([[16, 4, 0], [4, 0, 4], [64, 36, 16]], dtype=torch.float)
@@ -134,7 +134,7 @@ def test_computes_radial_basis_function_gradient(self):
         b = torch.tensor([0, 2, 2], dtype=torch.float).view(3, 1)
         lengthscale = 2
 
-        kernel = RBFKernel().initialize(log_lengthscale=math.log(lengthscale))
+        kernel = RBFKernel().initialize(lengthscale=lengthscale)
         kernel.eval()
 
         param = math.log(math.exp(lengthscale) - 1) * torch.ones(3, 3)
@@ -166,7 +166,7 @@ def test_subset_active_computes_radial_basis_function_gradient(self):
         actual_param_grad = param.grad.sum()
 
         kernel = RBFKernel(active_dims=[0])
-        kernel.initialize(log_lengthscale=math.log(lengthscale))
+        kernel.initialize(lengthscale=lengthscale)
         kernel.eval()
         output = kernel(a, b).evaluate()
         output.backward(gradient=torch.eye(3))
diff --git a/test/kernels/test_scale_kernel.py b/test/kernels/test_scale_kernel.py
index bdebd03fb..adcc5caaa 100644
--- a/test/kernels/test_scale_kernel.py
+++ b/test/kernels/test_scale_kernel.py
@@ -12,9 +12,9 @@ def test_ard(self):
         lengthscales = torch.tensor([1, 2], dtype=torch.float).view(1, 1, 2)
 
         base_kernel = RBFKernel(ard_num_dims=2)
-        base_kernel.initialize(log_lengthscale=lengthscales.log())
+        base_kernel.initialize(lengthscale=lengthscales)
         kernel = ScaleKernel(base_kernel)
-        kernel.initialize(log_outputscale=torch.tensor([3], dtype=torch.float).log())
+        kernel.initialize(outputscale=torch.tensor([3], dtype=torch.float))
         kernel.eval()
 
         scaled_a = a.div(lengthscales)
@@ -47,9 +47,9 @@ def test_ard_batch(self):
         lengthscales = torch.tensor([[[1, 2, 1]]], dtype=torch.float)
 
         base_kernel = RBFKernel(batch_size=2, ard_num_dims=3)
-        base_kernel.initialize(log_lengthscale=lengthscales.log())
+        base_kernel.initialize(lengthscale=lengthscales)
         kernel = ScaleKernel(base_kernel, batch_size=2)
-        kernel.initialize(log_outputscale=torch.tensor([1, 2], dtype=torch.float).log())
+        kernel.initialize(outputscale=torch.tensor([1, 2], dtype=torch.float))
         kernel.eval()
 
         scaled_a = a.div(lengthscales)
diff --git a/test/kernels/test_spectral_mixture_kernel.py b/test/kernels/test_spectral_mixture_kernel.py
index ab01b5490..40606b4ed 100644
--- a/test/kernels/test_spectral_mixture_kernel.py
+++ b/test/kernels/test_spectral_mixture_kernel.py
@@ -14,9 +14,9 @@ def test_standard(self):
         weights = [4, 2]
         kernel = SpectralMixtureKernel(num_mixtures=2, ard_num_dims=2)
         kernel.initialize(
-            log_mixture_weights=torch.tensor([[4, 2]], dtype=torch.float).log(),
-            log_mixture_means=torch.tensor([[[[1, 2]], [[2, 1]]]], dtype=torch.float).log(),
-            log_mixture_scales=torch.tensor([[[[0.5, 0.25]], [[0.25, 0.5]]]], dtype=torch.float).log(),
+            mixture_weights=torch.tensor([[4, 2]], dtype=torch.float),
+            mixture_means=torch.tensor([[[[1, 2]], [[2, 1]]]], dtype=torch.float),
+            mixture_scales=torch.tensor([[[[0.5, 0.25]], [[0.25, 0.5]]]], dtype=torch.float),
         )
         kernel.eval()
 
@@ -68,7 +68,7 @@ def test_batch_separate(self):
         weights = torch.tensor([[4, 2], [1, 2]], dtype=torch.float).view(2, 2)
         kernel = SpectralMixtureKernel(batch_size=2, num_mixtures=2)
         kernel.initialize(
-            log_mixture_weights=weights.log(), log_mixture_means=means.log(), log_mixture_scales=scales.log()
+            mixture_weights=weights, mixture_means=means, mixture_scales=scales
         )
         kernel.eval()
 

From 7f469e45ec78d7b65f48c6546c83ba87cb8df191 Mon Sep 17 00:00:00 2001
From: dme65 <dme65@cornell.edu>
Date: Sat, 26 Jan 2019 15:24:09 -0800
Subject: [PATCH 24/25] Adding size method to decorator kernels

---
 gpytorch/kernels/additive_structure_kernel.py | 3 +++
 gpytorch/kernels/grid_interpolation_kernel.py | 3 +++
 gpytorch/kernels/grid_kernel.py               | 3 +++
 gpytorch/kernels/inducing_point_kernel.py     | 3 +++
 gpytorch/kernels/kernel.py                    | 6 ++++++
 gpytorch/kernels/multi_device_kernel.py       | 3 +++
 gpytorch/kernels/product_structure_kernel.py  | 3 +++
 7 files changed, 24 insertions(+)

diff --git a/gpytorch/kernels/additive_structure_kernel.py b/gpytorch/kernels/additive_structure_kernel.py
index ddfc55478..34626d8b9 100644
--- a/gpytorch/kernels/additive_structure_kernel.py
+++ b/gpytorch/kernels/additive_structure_kernel.py
@@ -54,3 +54,6 @@ def forward(self, x1, x2, batch_dims=None, **params):
         if evaluate:
             res = res.evaluate()
         return res
+
+    def size(self, x1, x2):
+        return self.base_kernel.size(x1, x2)
diff --git a/gpytorch/kernels/grid_interpolation_kernel.py b/gpytorch/kernels/grid_interpolation_kernel.py
index 58f5fd58c..a56a9fdc7 100644
--- a/gpytorch/kernels/grid_interpolation_kernel.py
+++ b/gpytorch/kernels/grid_interpolation_kernel.py
@@ -180,3 +180,6 @@ def forward(self, x1, x2, batch_dims=None, **params):
         )
 
         return res
+
+    def size(self, x1, x2):
+        return self.base_kernel.size(x1, x2)
diff --git a/gpytorch/kernels/grid_kernel.py b/gpytorch/kernels/grid_kernel.py
index e3d884a9c..99615dfe8 100644
--- a/gpytorch/kernels/grid_kernel.py
+++ b/gpytorch/kernels/grid_kernel.py
@@ -98,3 +98,6 @@ def forward(self, x1, x2, diag=False, batch_dims=None, **params):
             return covar
         else:
             return self.base_kernel.forward(x1, x2, diag=diag, batch_dims=batch_dims, **params)
+
+    def size(self, x1, x2):
+        return self.base_kernel.size(x1, x2)
diff --git a/gpytorch/kernels/inducing_point_kernel.py b/gpytorch/kernels/inducing_point_kernel.py
index 699d347f5..81d0417f4 100644
--- a/gpytorch/kernels/inducing_point_kernel.py
+++ b/gpytorch/kernels/inducing_point_kernel.py
@@ -98,3 +98,6 @@ def forward(self, x1, x2, **kwargs):
             self.update_added_loss_term("inducing_point_loss_term", new_added_loss_term)
 
         return covar
+
+    def size(self, x1, x2):
+        return self.base_kernel.size(x1, x2)
diff --git a/gpytorch/kernels/kernel.py b/gpytorch/kernels/kernel.py
index b03cf3c5e..f4fcd5ab5 100644
--- a/gpytorch/kernels/kernel.py
+++ b/gpytorch/kernels/kernel.py
@@ -437,6 +437,9 @@ def forward(self, x1, x2, **params):
             res = res + lazify(next_term)
         return res
 
+    def size(self, x1, x2):
+        return self.kernels[0].size(x1, x2)
+
 
 class ProductKernel(Kernel):
     """
@@ -458,3 +461,6 @@ def forward(self, x1, x2, **params):
             next_term = kern(x1, x2, **params)
             res = res * lazify(next_term)
         return res
+
+    def size(self, x1, x2):
+        return self.kernels[0].size(x1, x2)
diff --git a/gpytorch/kernels/multi_device_kernel.py b/gpytorch/kernels/multi_device_kernel.py
index 96b404056..c692019f4 100644
--- a/gpytorch/kernels/multi_device_kernel.py
+++ b/gpytorch/kernels/multi_device_kernel.py
@@ -60,3 +60,6 @@ def forward(self, x1, x2, diag=False, **kwargs):
 
     def gather(self, outputs, output_device):
         return CatLazyTensor(*[lazify(o) for o in outputs], dim=self.dim, output_device=self.output_device)
+
+    def size(self, x1, x2):
+        return self.base_kernel.size(x1, x2)
diff --git a/gpytorch/kernels/product_structure_kernel.py b/gpytorch/kernels/product_structure_kernel.py
index 7c543e72f..39580b582 100644
--- a/gpytorch/kernels/product_structure_kernel.py
+++ b/gpytorch/kernels/product_structure_kernel.py
@@ -77,3 +77,6 @@ def __call__(self, x1_, x2_=None, diag=False, batch_dims=None, **params):
             .__call__(x1_, x2_, diag=diag, batch_dims=batch_dims, **params)
             .evaluate_kernel()
         )
+
+    def size(self, x1, x2):
+        return self.base_kernel.size(x1, x2)

From 5edcbaa9eb5d3d7f72adc3f70981a4b829924a8c Mon Sep 17 00:00:00 2001
From: Geoff Pleiss <gpleiss@gmail.com>
Date: Sun, 27 Jan 2019 18:22:50 -0500
Subject: [PATCH 25/25] Fix squeeze in RBFKernelGrad, update examples

---
 ...Regression_Derivative_Information_1d.ipynb | 129 ++++++++++--------
 ...Regression_Derivative_Information_2d.ipynb | 129 ++++++++++--------
 gpytorch/kernels/rbf_kernel_grad.py           |   2 +-
 3 files changed, 147 insertions(+), 113 deletions(-)

diff --git a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb
index d2af1f216..1c0e58b6a 100644
--- a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb
+++ b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_1d.ipynb
@@ -22,7 +22,16 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/gpleiss/miniconda3/envs/gpytorch/lib/python3.7/site-packages/matplotlib/__init__.py:1003: UserWarning: Duplicate key in file \"/home/gpleiss/.config/matplotlib/matplotlibrc\", line #57\n",
+      "  (fname, cnt))\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "import gpytorch\n",
@@ -108,60 +117,68 @@
    "execution_count": 4,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/gpleiss/workspace/gpytorch/gpytorch/utils/pivoted_cholesky.py:101: UserWarning: torch.potrs is deprecated in favour of torch.cholesky_solve and will be removed in the next release. Please use torch.cholesky instead and note that the :attr:`upper` argument in torch.cholesky_solve defaults to ``False``.\n",
+      "  R = torch.potrs(low_rank_mat, torch.cholesky(shifted_mat, upper=True))\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iter 1/50 - Loss: 61.582   log_lengthscale: -0.367   log_noise: -4.269\n",
-      "Iter 2/50 - Loss: 58.703   log_lengthscale: -0.295   log_noise: -4.269\n",
-      "Iter 3/50 - Loss: 56.608   log_lengthscale: -0.226   log_noise: -4.269\n",
-      "Iter 4/50 - Loss: 54.290   log_lengthscale: -0.160   log_noise: -4.269\n",
-      "Iter 5/50 - Loss: 52.452   log_lengthscale: -0.100   log_noise: -4.269\n",
-      "Iter 6/50 - Loss: 51.232   log_lengthscale: -0.046   log_noise: -4.269\n",
-      "Iter 7/50 - Loss: 48.236   log_lengthscale: -0.006   log_noise: -4.269\n",
-      "Iter 8/50 - Loss: 47.637   log_lengthscale: 0.017   log_noise: -4.269\n",
-      "Iter 9/50 - Loss: 45.793   log_lengthscale: 0.029   log_noise: -4.269\n",
-      "Iter 10/50 - Loss: 46.485   log_lengthscale: 0.031   log_noise: -4.269\n",
-      "Iter 11/50 - Loss: 42.307   log_lengthscale: 0.029   log_noise: -4.269\n",
-      "Iter 12/50 - Loss: 40.820   log_lengthscale: 0.023   log_noise: -4.269\n",
-      "Iter 13/50 - Loss: 39.255   log_lengthscale: 0.014   log_noise: -4.269\n",
-      "Iter 14/50 - Loss: 36.660   log_lengthscale: 0.011   log_noise: -4.269\n",
-      "Iter 15/50 - Loss: 34.557   log_lengthscale: 0.012   log_noise: -4.269\n",
-      "Iter 16/50 - Loss: 33.867   log_lengthscale: 0.014   log_noise: -4.269\n",
-      "Iter 17/50 - Loss: 31.727   log_lengthscale: 0.016   log_noise: -4.269\n",
-      "Iter 18/50 - Loss: 27.939   log_lengthscale: 0.027   log_noise: -4.269\n",
-      "Iter 19/50 - Loss: 27.122   log_lengthscale: 0.042   log_noise: -4.269\n",
-      "Iter 20/50 - Loss: 24.861   log_lengthscale: 0.063   log_noise: -4.269\n",
-      "Iter 21/50 - Loss: 23.606   log_lengthscale: 0.083   log_noise: -4.269\n",
-      "Iter 22/50 - Loss: 21.119   log_lengthscale: 0.103   log_noise: -4.269\n",
-      "Iter 23/50 - Loss: 19.037   log_lengthscale: 0.114   log_noise: -4.269\n",
-      "Iter 24/50 - Loss: 16.896   log_lengthscale: 0.121   log_noise: -4.269\n",
-      "Iter 25/50 - Loss: 20.047   log_lengthscale: 0.122   log_noise: -4.269\n",
-      "Iter 26/50 - Loss: 15.217   log_lengthscale: 0.127   log_noise: -4.269\n",
-      "Iter 27/50 - Loss: 14.856   log_lengthscale: 0.133   log_noise: -4.269\n",
-      "Iter 28/50 - Loss: 11.275   log_lengthscale: 0.141   log_noise: -4.269\n",
-      "Iter 29/50 - Loss: 12.726   log_lengthscale: 0.152   log_noise: -4.269\n",
-      "Iter 30/50 - Loss: 5.189   log_lengthscale: 0.160   log_noise: -4.269\n",
-      "Iter 31/50 - Loss: 6.020   log_lengthscale: 0.161   log_noise: -4.269\n",
-      "Iter 32/50 - Loss: 4.733   log_lengthscale: 0.159   log_noise: -4.269\n",
-      "Iter 33/50 - Loss: 4.649   log_lengthscale: 0.158   log_noise: -4.269\n",
-      "Iter 34/50 - Loss: -0.154   log_lengthscale: 0.165   log_noise: -4.269\n",
-      "Iter 35/50 - Loss: 2.789   log_lengthscale: 0.173   log_noise: -4.269\n",
-      "Iter 36/50 - Loss: -0.807   log_lengthscale: 0.187   log_noise: -4.269\n",
-      "Iter 37/50 - Loss: 1.525   log_lengthscale: 0.207   log_noise: -4.269\n",
-      "Iter 38/50 - Loss: -2.236   log_lengthscale: 0.221   log_noise: -4.269\n",
-      "Iter 39/50 - Loss: -0.497   log_lengthscale: 0.227   log_noise: -4.269\n",
-      "Iter 40/50 - Loss: -4.761   log_lengthscale: 0.225   log_noise: -4.269\n",
-      "Iter 41/50 - Loss: -7.380   log_lengthscale: 0.214   log_noise: -4.269\n",
-      "Iter 42/50 - Loss: -5.682   log_lengthscale: 0.199   log_noise: -4.269\n",
-      "Iter 43/50 - Loss: -8.120   log_lengthscale: 0.188   log_noise: -4.269\n",
-      "Iter 44/50 - Loss: -7.820   log_lengthscale: 0.174   log_noise: -4.269\n",
-      "Iter 45/50 - Loss: -7.840   log_lengthscale: 0.168   log_noise: -4.269\n",
-      "Iter 46/50 - Loss: -9.724   log_lengthscale: 0.170   log_noise: -4.269\n",
-      "Iter 47/50 - Loss: -7.647   log_lengthscale: 0.183   log_noise: -4.269\n",
-      "Iter 48/50 - Loss: -10.614   log_lengthscale: 0.201   log_noise: -4.269\n",
-      "Iter 49/50 - Loss: -13.871   log_lengthscale: 0.220   log_noise: -4.269\n",
-      "Iter 50/50 - Loss: -15.149   log_lengthscale: 0.233   log_noise: -4.269\n"
+      "Iter 1/50 - Loss: 60.662   lengthscale: 0.693   noise: 0.014\n",
+      "Iter 2/50 - Loss: 58.924   lengthscale: 0.744   noise: 0.014\n",
+      "Iter 3/50 - Loss: 57.026   lengthscale: 0.798   noise: 0.014\n",
+      "Iter 4/50 - Loss: 55.471   lengthscale: 0.853   noise: 0.014\n",
+      "Iter 5/50 - Loss: 52.139   lengthscale: 0.905   noise: 0.014\n",
+      "Iter 6/50 - Loss: 51.552   lengthscale: 0.950   noise: 0.014\n",
+      "Iter 7/50 - Loss: 50.446   lengthscale: 0.988   noise: 0.014\n",
+      "Iter 8/50 - Loss: 46.764   lengthscale: 1.015   noise: 0.014\n",
+      "Iter 9/50 - Loss: 48.350   lengthscale: 1.028   noise: 0.014\n",
+      "Iter 10/50 - Loss: 43.598   lengthscale: 1.036   noise: 0.014\n",
+      "Iter 11/50 - Loss: 42.887   lengthscale: 1.037   noise: 0.014\n",
+      "Iter 12/50 - Loss: 42.121   lengthscale: 1.032   noise: 0.014\n",
+      "Iter 13/50 - Loss: 39.327   lengthscale: 1.026   noise: 0.014\n",
+      "Iter 14/50 - Loss: 38.159   lengthscale: 1.020   noise: 0.014\n",
+      "Iter 15/50 - Loss: 36.058   lengthscale: 1.018   noise: 0.014\n",
+      "Iter 16/50 - Loss: 33.545   lengthscale: 1.019   noise: 0.014\n",
+      "Iter 17/50 - Loss: 30.586   lengthscale: 1.024   noise: 0.014\n",
+      "Iter 18/50 - Loss: 29.251   lengthscale: 1.036   noise: 0.014\n",
+      "Iter 19/50 - Loss: 24.608   lengthscale: 1.053   noise: 0.014\n",
+      "Iter 20/50 - Loss: 25.399   lengthscale: 1.069   noise: 0.014\n",
+      "Iter 21/50 - Loss: 22.303   lengthscale: 1.091   noise: 0.014\n",
+      "Iter 22/50 - Loss: 19.724   lengthscale: 1.119   noise: 0.014\n",
+      "Iter 23/50 - Loss: 22.927   lengthscale: 1.141   noise: 0.014\n",
+      "Iter 24/50 - Loss: 19.074   lengthscale: 1.164   noise: 0.014\n",
+      "Iter 25/50 - Loss: 17.078   lengthscale: 1.176   noise: 0.014\n",
+      "Iter 26/50 - Loss: 14.719   lengthscale: 1.181   noise: 0.014\n",
+      "Iter 27/50 - Loss: 14.614   lengthscale: 1.183   noise: 0.014\n",
+      "Iter 28/50 - Loss: 10.786   lengthscale: 1.180   noise: 0.014\n",
+      "Iter 29/50 - Loss: 9.239   lengthscale: 1.166   noise: 0.014\n",
+      "Iter 30/50 - Loss: 6.483   lengthscale: 1.151   noise: 0.014\n",
+      "Iter 31/50 - Loss: 6.814   lengthscale: 1.141   noise: 0.014\n",
+      "Iter 32/50 - Loss: 4.227   lengthscale: 1.146   noise: 0.014\n",
+      "Iter 33/50 - Loss: 4.655   lengthscale: 1.158   noise: 0.014\n",
+      "Iter 34/50 - Loss: 2.414   lengthscale: 1.174   noise: 0.014\n",
+      "Iter 35/50 - Loss: 2.902   lengthscale: 1.190   noise: 0.014\n",
+      "Iter 36/50 - Loss: -3.138   lengthscale: 1.209   noise: 0.014\n",
+      "Iter 37/50 - Loss: 2.241   lengthscale: 1.228   noise: 0.014\n",
+      "Iter 38/50 - Loss: -6.051   lengthscale: 1.241   noise: 0.014\n",
+      "Iter 39/50 - Loss: -3.690   lengthscale: 1.251   noise: 0.014\n",
+      "Iter 40/50 - Loss: -2.811   lengthscale: 1.256   noise: 0.014\n",
+      "Iter 41/50 - Loss: -9.964   lengthscale: 1.259   noise: 0.014\n",
+      "Iter 42/50 - Loss: -5.040   lengthscale: 1.253   noise: 0.014\n",
+      "Iter 43/50 - Loss: -9.899   lengthscale: 1.250   noise: 0.014\n",
+      "Iter 44/50 - Loss: -10.464   lengthscale: 1.241   noise: 0.014\n",
+      "Iter 45/50 - Loss: -8.486   lengthscale: 1.234   noise: 0.014\n",
+      "Iter 46/50 - Loss: -9.840   lengthscale: 1.231   noise: 0.014\n",
+      "Iter 47/50 - Loss: -12.451   lengthscale: 1.235   noise: 0.014\n",
+      "Iter 48/50 - Loss: -12.304   lengthscale: 1.246   noise: 0.014\n",
+      "Iter 49/50 - Loss: -7.173   lengthscale: 1.257   noise: 0.014\n",
+      "Iter 50/50 - Loss: -11.317   lengthscale: 1.268   noise: 0.014\n"
      ]
     }
    ],
@@ -184,10 +201,10 @@
     "    output = model(train_x)\n",
     "    loss = -mll(output, train_y)\n",
     "    loss.backward()\n",
-    "    print('Iter %d/%d - Loss: %.3f   log_lengthscale: %.3f   log_noise: %.3f' % (\n",
+    "    print('Iter %d/%d - Loss: %.3f   lengthscale: %.3f   noise: %.3f' % (\n",
     "        i + 1, n_iter, loss.item(),\n",
-    "        model.covar_module.base_kernel.log_lengthscale.item(),\n",
-    "        model.likelihood.log_noise.item()\n",
+    "        model.covar_module.base_kernel.lengthscale.item(),\n",
+    "        model.likelihood.noise.item()\n",
     "    ))        \n",
     "    optimizer.step()"
    ]
@@ -207,7 +224,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x432 with 2 Axes>"
       ]
@@ -279,7 +296,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.7.1"
   }
  },
  "nbformat": 4,
diff --git a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb
index ee9fab38c..2a043055c 100644
--- a/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb
+++ b/examples/10_GP_Regression_Derivative_Information/Simple_GP_Regression_Derivative_Information_2d.ipynb
@@ -14,7 +14,16 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/gpleiss/miniconda3/envs/gpytorch/lib/python3.7/site-packages/matplotlib/__init__.py:1003: UserWarning: Duplicate key in file \"/home/gpleiss/.config/matplotlib/matplotlibrc\", line #57\n",
+      "  (fname, cnt))\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "import gpytorch\n",
@@ -132,60 +141,68 @@
    "execution_count": 5,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/gpleiss/workspace/gpytorch/gpytorch/utils/pivoted_cholesky.py:101: UserWarning: torch.potrs is deprecated in favour of torch.cholesky_solve and will be removed in the next release. Please use torch.cholesky instead and note that the :attr:`upper` argument in torch.cholesky_solve defaults to ``False``.\n",
+      "  R = torch.potrs(low_rank_mat, torch.cholesky(shifted_mat, upper=True))\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iter 1/50 - Loss: 111.304   log_lengthscale: -0.367   log_noise: -4.705\n",
-      "Iter 2/50 - Loss: 108.440   log_lengthscale: -0.439   log_noise: -4.705\n",
-      "Iter 3/50 - Loss: 104.407   log_lengthscale: -0.514   log_noise: -4.705\n",
-      "Iter 4/50 - Loss: 98.889   log_lengthscale: -0.590   log_noise: -4.705\n",
-      "Iter 5/50 - Loss: 96.811   log_lengthscale: -0.668   log_noise: -4.705\n",
-      "Iter 6/50 - Loss: 92.089   log_lengthscale: -0.748   log_noise: -4.705\n",
-      "Iter 7/50 - Loss: 88.659   log_lengthscale: -0.829   log_noise: -4.705\n",
-      "Iter 8/50 - Loss: 82.004   log_lengthscale: -0.912   log_noise: -4.705\n",
-      "Iter 9/50 - Loss: 79.140   log_lengthscale: -0.996   log_noise: -4.705\n",
-      "Iter 10/50 - Loss: 75.356   log_lengthscale: -1.081   log_noise: -4.705\n",
-      "Iter 11/50 - Loss: 71.968   log_lengthscale: -1.160   log_noise: -4.705\n",
-      "Iter 12/50 - Loss: 70.328   log_lengthscale: -1.230   log_noise: -4.705\n",
-      "Iter 13/50 - Loss: 68.670   log_lengthscale: -1.273   log_noise: -4.705\n",
-      "Iter 14/50 - Loss: 63.437   log_lengthscale: -1.291   log_noise: -4.705\n",
-      "Iter 15/50 - Loss: 59.861   log_lengthscale: -1.284   log_noise: -4.705\n",
-      "Iter 16/50 - Loss: 53.896   log_lengthscale: -1.262   log_noise: -4.705\n",
-      "Iter 17/50 - Loss: 52.225   log_lengthscale: -1.234   log_noise: -4.705\n",
-      "Iter 18/50 - Loss: 45.478   log_lengthscale: -1.201   log_noise: -4.705\n",
-      "Iter 19/50 - Loss: 46.308   log_lengthscale: -1.174   log_noise: -4.705\n",
-      "Iter 20/50 - Loss: 41.343   log_lengthscale: -1.151   log_noise: -4.705\n",
-      "Iter 21/50 - Loss: 42.311   log_lengthscale: -1.142   log_noise: -4.705\n",
-      "Iter 22/50 - Loss: 35.236   log_lengthscale: -1.149   log_noise: -4.705\n",
-      "Iter 23/50 - Loss: 29.793   log_lengthscale: -1.171   log_noise: -4.705\n",
-      "Iter 24/50 - Loss: 22.766   log_lengthscale: -1.208   log_noise: -4.705\n",
-      "Iter 25/50 - Loss: 23.147   log_lengthscale: -1.249   log_noise: -4.705\n",
-      "Iter 26/50 - Loss: 22.978   log_lengthscale: -1.294   log_noise: -4.705\n",
-      "Iter 27/50 - Loss: 19.436   log_lengthscale: -1.325   log_noise: -4.705\n",
-      "Iter 28/50 - Loss: 13.901   log_lengthscale: -1.340   log_noise: -4.705\n",
-      "Iter 29/50 - Loss: 11.534   log_lengthscale: -1.340   log_noise: -4.705\n",
-      "Iter 30/50 - Loss: 4.914   log_lengthscale: -1.325   log_noise: -4.705\n",
-      "Iter 31/50 - Loss: 2.254   log_lengthscale: -1.305   log_noise: -4.705\n",
-      "Iter 32/50 - Loss: -1.311   log_lengthscale: -1.288   log_noise: -4.705\n",
-      "Iter 33/50 - Loss: -3.087   log_lengthscale: -1.287   log_noise: -4.705\n",
-      "Iter 34/50 - Loss: -3.262   log_lengthscale: -1.296   log_noise: -4.705\n",
-      "Iter 35/50 - Loss: -10.622   log_lengthscale: -1.312   log_noise: -4.705\n",
-      "Iter 36/50 - Loss: -15.679   log_lengthscale: -1.337   log_noise: -4.705\n",
-      "Iter 37/50 - Loss: -17.539   log_lengthscale: -1.366   log_noise: -4.705\n",
-      "Iter 38/50 - Loss: -15.919   log_lengthscale: -1.401   log_noise: -4.705\n",
-      "Iter 39/50 - Loss: -15.652   log_lengthscale: -1.425   log_noise: -4.705\n",
-      "Iter 40/50 - Loss: -17.698   log_lengthscale: -1.436   log_noise: -4.705\n",
-      "Iter 41/50 - Loss: -20.385   log_lengthscale: -1.436   log_noise: -4.705\n",
-      "Iter 42/50 - Loss: -18.727   log_lengthscale: -1.438   log_noise: -4.705\n",
-      "Iter 43/50 - Loss: -22.506   log_lengthscale: -1.446   log_noise: -4.705\n",
-      "Iter 44/50 - Loss: -28.562   log_lengthscale: -1.452   log_noise: -4.705\n",
-      "Iter 45/50 - Loss: -31.806   log_lengthscale: -1.462   log_noise: -4.705\n",
-      "Iter 46/50 - Loss: -31.099   log_lengthscale: -1.480   log_noise: -4.705\n",
-      "Iter 47/50 - Loss: -32.806   log_lengthscale: -1.506   log_noise: -4.705\n",
-      "Iter 48/50 - Loss: -33.661   log_lengthscale: -1.529   log_noise: -4.705\n",
-      "Iter 49/50 - Loss: -32.647   log_lengthscale: -1.549   log_noise: -4.705\n",
-      "Iter 50/50 - Loss: -36.437   log_lengthscale: -1.567   log_noise: -4.705\n"
+      "Iter 1/50 - Loss: 110.599   lengthscale: 0.693   noise: 0.009\n",
+      "Iter 2/50 - Loss: 108.303   lengthscale: 0.644   noise: 0.009\n",
+      "Iter 3/50 - Loss: 103.957   lengthscale: 0.598   noise: 0.009\n",
+      "Iter 4/50 - Loss: 99.515   lengthscale: 0.554   noise: 0.009\n",
+      "Iter 5/50 - Loss: 95.034   lengthscale: 0.513   noise: 0.009\n",
+      "Iter 6/50 - Loss: 92.725   lengthscale: 0.473   noise: 0.009\n",
+      "Iter 7/50 - Loss: 89.589   lengthscale: 0.436   noise: 0.009\n",
+      "Iter 8/50 - Loss: 82.739   lengthscale: 0.402   noise: 0.009\n",
+      "Iter 9/50 - Loss: 76.709   lengthscale: 0.370   noise: 0.009\n",
+      "Iter 10/50 - Loss: 74.856   lengthscale: 0.340   noise: 0.009\n",
+      "Iter 11/50 - Loss: 69.440   lengthscale: 0.313   noise: 0.009\n",
+      "Iter 12/50 - Loss: 69.494   lengthscale: 0.291   noise: 0.009\n",
+      "Iter 13/50 - Loss: 70.631   lengthscale: 0.277   noise: 0.009\n",
+      "Iter 14/50 - Loss: 65.169   lengthscale: 0.272   noise: 0.009\n",
+      "Iter 15/50 - Loss: 61.315   lengthscale: 0.273   noise: 0.009\n",
+      "Iter 16/50 - Loss: 54.774   lengthscale: 0.279   noise: 0.009\n",
+      "Iter 17/50 - Loss: 50.667   lengthscale: 0.287   noise: 0.009\n",
+      "Iter 18/50 - Loss: 45.541   lengthscale: 0.297   noise: 0.009\n",
+      "Iter 19/50 - Loss: 42.602   lengthscale: 0.306   noise: 0.009\n",
+      "Iter 20/50 - Loss: 42.981   lengthscale: 0.313   noise: 0.009\n",
+      "Iter 21/50 - Loss: 36.291   lengthscale: 0.316   noise: 0.009\n",
+      "Iter 22/50 - Loss: 35.341   lengthscale: 0.315   noise: 0.009\n",
+      "Iter 23/50 - Loss: 30.169   lengthscale: 0.309   noise: 0.009\n",
+      "Iter 24/50 - Loss: 26.163   lengthscale: 0.301   noise: 0.009\n",
+      "Iter 25/50 - Loss: 21.606   lengthscale: 0.290   noise: 0.009\n",
+      "Iter 26/50 - Loss: 20.486   lengthscale: 0.279   noise: 0.009\n",
+      "Iter 27/50 - Loss: 17.299   lengthscale: 0.270   noise: 0.009\n",
+      "Iter 28/50 - Loss: 14.477   lengthscale: 0.265   noise: 0.009\n",
+      "Iter 29/50 - Loss: 12.003   lengthscale: 0.265   noise: 0.009\n",
+      "Iter 30/50 - Loss: 5.255   lengthscale: 0.268   noise: 0.009\n",
+      "Iter 31/50 - Loss: 3.595   lengthscale: 0.270   noise: 0.009\n",
+      "Iter 32/50 - Loss: 0.765   lengthscale: 0.270   noise: 0.009\n",
+      "Iter 33/50 - Loss: -5.474   lengthscale: 0.270   noise: 0.009\n",
+      "Iter 34/50 - Loss: -8.094   lengthscale: 0.268   noise: 0.009\n",
+      "Iter 35/50 - Loss: -8.584   lengthscale: 0.264   noise: 0.009\n",
+      "Iter 36/50 - Loss: -16.166   lengthscale: 0.258   noise: 0.009\n",
+      "Iter 37/50 - Loss: -16.839   lengthscale: 0.251   noise: 0.009\n",
+      "Iter 38/50 - Loss: -16.507   lengthscale: 0.245   noise: 0.009\n",
+      "Iter 39/50 - Loss: -19.224   lengthscale: 0.242   noise: 0.009\n",
+      "Iter 40/50 - Loss: -21.473   lengthscale: 0.240   noise: 0.009\n",
+      "Iter 41/50 - Loss: -22.681   lengthscale: 0.238   noise: 0.009\n",
+      "Iter 42/50 - Loss: -27.049   lengthscale: 0.234   noise: 0.009\n",
+      "Iter 43/50 - Loss: -25.780   lengthscale: 0.233   noise: 0.009\n",
+      "Iter 44/50 - Loss: -26.668   lengthscale: 0.233   noise: 0.009\n",
+      "Iter 45/50 - Loss: -30.996   lengthscale: 0.234   noise: 0.009\n",
+      "Iter 46/50 - Loss: -34.794   lengthscale: 0.233   noise: 0.009\n",
+      "Iter 47/50 - Loss: -34.021   lengthscale: 0.228   noise: 0.009\n",
+      "Iter 48/50 - Loss: -34.319   lengthscale: 0.222   noise: 0.009\n",
+      "Iter 49/50 - Loss: -34.365   lengthscale: 0.215   noise: 0.009\n",
+      "Iter 50/50 - Loss: -31.525   lengthscale: 0.209   noise: 0.009\n"
      ]
     }
    ],
@@ -208,10 +225,10 @@
     "    output = model(train_x)\n",
     "    loss = -mll(output, train_y)\n",
     "    loss.backward()\n",
-    "    print('Iter %d/%d - Loss: %.3f   log_lengthscale: %.3f   log_noise: %.3f' % (\n",
+    "    print('Iter %d/%d - Loss: %.3f   lengthscale: %.3f   noise: %.3f' % (\n",
     "        i + 1, n_iter, loss.item(),\n",
-    "        model.covar_module.base_kernel.log_lengthscale.item(),\n",
-    "        model.likelihood.log_noise.item()\n",
+    "        model.covar_module.base_kernel.lengthscale.item(),\n",
+    "        model.likelihood.noise.item()\n",
     "    ))\n",
     "    optimizer.step()"
    ]
@@ -231,7 +248,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1008x720 with 6 Axes>"
       ]
@@ -303,7 +320,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.7.1"
   }
  },
  "nbformat": 4,
diff --git a/gpytorch/kernels/rbf_kernel_grad.py b/gpytorch/kernels/rbf_kernel_grad.py
index f195f5291..d873980ca 100644
--- a/gpytorch/kernels/rbf_kernel_grad.py
+++ b/gpytorch/kernels/rbf_kernel_grad.py
@@ -85,7 +85,7 @@ def forward(self, x1, x2, diag=False, **params):
             _, n2, _ = x2.size()
 
         K = torch.zeros(b, n1 * (d + 1), n2 * (d + 1), device=x1.device, dtype=x1.dtype)  # batch x n1(d+1) x n2(d+1)
-        ell = self.lengthscale.squeeze()
+        ell = self.lengthscale.squeeze(-1)
 
         if not diag:
             # Scale the inputs by the lengthscale (for stability)