diff --git a/examples/pytorch_sparse_quantized_transfer_learning/pytorch_sparse_quantized_transfer_learning.ipynb b/examples/pytorch_sparse_quantized_transfer_learning/pytorch_sparse_quantized_transfer_learning.ipynb
index b46729de0ce..63002433da0 100644
--- a/examples/pytorch_sparse_quantized_transfer_learning/pytorch_sparse_quantized_transfer_learning.ipynb
+++ b/examples/pytorch_sparse_quantized_transfer_learning/pytorch_sparse_quantized_transfer_learning.ipynb
@@ -332,13 +332,13 @@
     "from sparseml.pytorch.utils import ModuleExporter\n",
     "\n",
     "save_dir = \"pytorch_sparse_quantized_transfer_learning\"\n",
-    "qat_onnx_graph_name = \"resnet50_imagenette_pruned_qat.onnx\"\n",
-    "quantized_onnx_path = os.path.join(save_dir, \"resnet50_imagenette_pruned_quant.onnx\")\n",
+    "quant_onnx_graph_name = \"resnet50_imagenette_pruned_quant.onnx\"\n",
+    "quantized_onnx_path = os.path.join(save_dir, quant_onnx_graph_name)\n",
     "\n",
     "exporter = ModuleExporter(model, output_dir=save_dir)\n",
     "exporter.export_pytorch(name=\"resnet50_imagenette_pruned_qat.pth\")\n",
     "exporter.export_onnx(\n",
-    "    torch.randn(1, 3, 224, 224), name=qat_onnx_graph_name, convert_qat=True\n",
+    "    torch.randn(1, 3, 224, 224), name=quant_onnx_graph_name, convert_qat=True\n",
     ")\n",
     "\n",
     "print(f\"Sparse-Quantized ONNX model saved to {quantized_onnx_path}\")"
diff --git a/src/sparseml/pytorch/optim/manager.py b/src/sparseml/pytorch/optim/manager.py
index 89e7561c610..c0e1b1821b1 100644
--- a/src/sparseml/pytorch/optim/manager.py
+++ b/src/sparseml/pytorch/optim/manager.py
@@ -158,6 +158,43 @@ def step(self, *args, **kwargs):
         :param kwargs: Any kwargs to pass to the wrapped objects step function.
         :return: The return, if any, from the wrapped objects step function
         """
+        return self._perform_wrapped_step(*args, **kwargs)
+
+    def emulated_step(self):
+        """
+        Emulated step function to be called in place of step when the
+        number of steps_per_epoch vary across epochs.
+        The emulated function should be called to keep the steps_per_epoch thee same.
+        Does not call into the step function for the wrapped object,
+        but does call into the manager to increment the steps.
+        """
+        self._perform_wrapped_step(skip_orig_step=True)
+
+    def loss_update(self, loss: Tensor) -> Tensor:
+        """
+        Optional call to update modifiers based on the calculated loss.
+        Not needed unless one or more of the modifier is using the loss
+        to make a modification or is modifying the loss itself.
+
+        :param loss: the calculated loss after running a forward pass and loss_fn
+        :return: the modified loss tensor
+        """
+        loss = self._wrapped_manager.loss_update(
+            loss,
+            self._wrapped_module,
+            self._wrapped_optimizer,
+            self._wrapped_epoch,
+            self._wrapped_steps_per_epoch,
+        )
+
+        return loss
+
+    def _perform_wrapped_step(self, *args, **kwargs) -> Any:
+        skip_orig_step = (
+            kwargs["skip_orig_step"] if "skip_orig_step" in kwargs else False
+        )
+        ret = None
+
         if self._wrapped_manager.enabled:
             self._wrapped_manager.update(
                 self._wrapped_module,
@@ -172,7 +209,8 @@ def step(self, *args, **kwargs):
                 self._wrapped_steps_per_epoch,
             )
 
-        ret = self._wrapped.step(*args, **kwargs)
+        if not skip_orig_step:
+            ret = self._wrapped.step(*args, **kwargs)
 
         if self._wrapped_manager.enabled:
             self._wrapped_manager.optimizer_post_step(
@@ -192,25 +230,6 @@ def step(self, *args, **kwargs):
 
         return ret
 
-    def loss_update(self, loss: Tensor) -> Tensor:
-        """
-        Optional call to update modifiers based on the calculated loss.
-        Not needed unless one or more of the modifier is using the loss
-        to make a modification or is modifying the loss itself.
-
-        :param loss: the calculated loss after running a forward pass and loss_fn
-        :return: the modified loss tensor
-        """
-        loss = self._wrapped_manager.loss_update(
-            loss,
-            self._wrapped_module,
-            self._wrapped_optimizer,
-            self._wrapped_epoch,
-            self._wrapped_steps_per_epoch,
-        )
-
-        return loss
-
 
 class ScheduledModifierManager(BaseManager, Modifier):
     """