diff --git a/bindsnet/datasets/torchvision_wrapper.py b/bindsnet/datasets/torchvision_wrapper.py
index 82ce3b40e..8bb466d73 100644
--- a/bindsnet/datasets/torchvision_wrapper.py
+++ b/bindsnet/datasets/torchvision_wrapper.py
@@ -31,8 +31,8 @@ class torchvision_dataset_wrapper(ds_type):
 
         def __init__(
             self,
-            image_encoder: Optional[Encoder],
-            label_encoder: Optional[Encoder],
+            image_encoder: Optional[Encoder] = None,
+            label_encoder: Optional[Encoder] = None,
             *args,
             **kwargs
         ):
diff --git a/examples/mnist/eth_mnist.py b/examples/mnist/eth_mnist.py
index e16b3d8c3..62be621df 100644
--- a/examples/mnist/eth_mnist.py
+++ b/examples/mnist/eth_mnist.py
@@ -4,9 +4,12 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
+from torchvision import transforms
+
 from time import time as t
 
 from bindsnet.datasets import MNIST
+from bindsnet.encoding import PoissonEncoder
 from bindsnet.encoding import poisson_loader
 from bindsnet.models import DiehlAndCook2015
 from bindsnet.network.monitors import Monitor
@@ -80,11 +83,30 @@
 network.add_monitor(inh_voltage_monitor, name="inh_voltage")
 
 # Load MNIST data.
-images, labels = MNIST(
-    path=os.path.join("..", "..", "data", "MNIST"), download=True
-).get_train()
-images = images.view(-1, 784)
-images *= intensity
+# images, labels
+dataset = MNIST(
+    None,
+    None,
+    root=os.path.join("..", "..", "data", "MNIST"),
+    download=True,
+    transform=transforms.Compose(
+        [
+            transforms.ToTensor(),
+            transforms.Lambda(lambda x: x * intensity),
+            transforms.Lambda(lambda x: x.view(784)),
+        ]
+    ),
+)
+# .get_train()
+
+images = []
+labels = []
+
+for i in range(len(dataset)):
+    sample = dataset[i]
+    images.append(sample["image"])
+    labels.append(sample["label"])
+
 
 # Lazily encode data as Poisson spike trains.
 data_loader = poisson_loader(data=images, time=time, dt=dt)