From aeeb9c74fb7811fbea3bb189179f1ebe861d76ef Mon Sep 17 00:00:00 2001
From: Valeh Valiollah Pour Amiri <4193454+watiss@users.noreply.github.com>
Date: Wed, 29 Dec 2021 09:30:29 -0800
Subject: [PATCH 1/2] Fix `total_counts` to `total_count`

Fix `total_counts` to `total_count` in the module_user_guide
---
 module_user_guide.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/module_user_guide.ipynb b/module_user_guide.ipynb
index f31b51a2..6743ed72 100644
--- a/module_user_guide.ipynb
+++ b/module_user_guide.ipynb
@@ -411,7 +411,7 @@
     "        # the pytorch NB distribution uses a different parameterization\n",
     "        # so we must apply a quick transformation (included in scvi-tools, but here we use the pytorch code)\n",
     "        nb_logits = (px_rate + 1e-4).log() - (theta + 1e-4).log()\n",
-    "        log_lik = NegativeBinomial(total_counts=theta, total=nb_logits).log_prob(x).sum(dim=-1)    \n",
+    "        log_lik = NegativeBinomial(total_count=theta, total=nb_logits).log_prob(x).sum(dim=-1)    \n",
     "\n",
     "        # term 2\n",
     "        prior_dist = Normal(torch.zeros_like(qz_m), torch.ones_like(qz_v))\n",

From f729c111f37d3538bcac25a41e030d9b5123da62 Mon Sep 17 00:00:00 2001
From: Valeh Valiollah Pour Amiri <4193454+watiss@users.noreply.github.com>
Date: Sun, 2 Jan 2022 10:35:07 -0800
Subject: [PATCH 2/2] Replace `total` with `logits`

---
 module_user_guide.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/module_user_guide.ipynb b/module_user_guide.ipynb
index 6743ed72..62c9558e 100644
--- a/module_user_guide.ipynb
+++ b/module_user_guide.ipynb
@@ -411,7 +411,7 @@
     "        # the pytorch NB distribution uses a different parameterization\n",
     "        # so we must apply a quick transformation (included in scvi-tools, but here we use the pytorch code)\n",
     "        nb_logits = (px_rate + 1e-4).log() - (theta + 1e-4).log()\n",
-    "        log_lik = NegativeBinomial(total_count=theta, total=nb_logits).log_prob(x).sum(dim=-1)    \n",
+    "        log_lik = NegativeBinomial(total_count=theta, logits=nb_logits).log_prob(x).sum(dim=-1)    \n",
     "\n",
     "        # term 2\n",
     "        prior_dist = Normal(torch.zeros_like(qz_m), torch.ones_like(qz_v))\n",