Here is how you can visualize the dataset using torchvision grid:

import matplotlib.pyplot as plt
import torchvision

# The resolution of the resized images is 64 x 64
img_resolution = (64, 64)

# We can create an iterator using the dataloaders and take a random sample
images, labels = next(iter(trainloader))

# You may need to reshape the images batch to 16 x 1 x 64 x 64
# Assuming the images are grayscale, if they are RGB, the second dimension should be 3
images = images.view(images.shape[0], -1, img_resolution[0], img_resolution[1])

# Visualize dataset using torchvision grid
# Make a grid from batch
grid = torchvision.utils.make_grid(images, nrow=4)

# Convert tensor to numpy for visualization
grid = grid.numpy().transpose((1, 2, 0))

# Show images
plt.imshow(grid)
plt.axis('off')
plt.show()

Please note that the reshaping of the images depends on the original shape of your images. If your images are not grayscale, you should adjust the reshaping accordingly. The nrow parameter in make_grid function specifies how many images per row to display. You can adjust it based on your preference.