This code accomplishes the following: 1. **Create a Dictionary for Storing...

This code accomplishes the following:

1. Create a Dictionary for Storing Examples:

   • A dictionary named class_examples is initialized as an empty dictionary: class_examples = {}.
   • Its purpose is to store a single example (e.g., an image) for each class, where the key is the class label (e.g., label) and the value is the corresponding image (img).

2. Prepare for Plotting:

   • A matplotlib figure (fig) and a set of subplots (axes) are created using plt.subplots. The number of subplots is determined by the length of the class_examples dictionary (i.e., the number of classes). If there are n classes, then plt.subplots creates a single row (1) with n subplots.
   • These subplots are arranged horizontally, and the figure's size is set using figsize=(15, 5).

3. Iterate Over Class Examples:

   • The enumerate function is used to iterate over the items (key-value pairs) in the class_examples dictionary. For each iteration:
     • label: Represents the class label.
     • img: Represents the corresponding image for that class.

4. Display Images in Subplots:

   • Each image (img) is plotted on its respective axis (axes[idx]) using imshow.
   • Before plotting, img.permute(1, 2, 0) is called to rearrange the dimensions of the image tensor. This transformation is common when working with PyTorch tensors.
     • PyTorch images are typically in the format (C, H, W) (Channels, Height, Width), but imshow requires the image to be in the format (H, W, C) (Height, Width, Channels). permute(1, 2, 0) swaps the dimensions accordingly.

Summary:

• This code creates a dictionary of one image per class.
• Then, it plots all the example images (one per class) in a single row of subplots. Each image is arranged according to its class label. The images are formatted for display using tensor dimension rearrangement.