transforms.Compose

transforms.Compose is a PyTorch function that allows you to apply multiple transforms to a given dataset. It takes a list of transforms as input and applies them in sequence to the input data.

For example, if you have an image dataset and you want to apply the following transformations:

- Resize the images to 256x256
- Convert the images to grayscale
- Normalize the pixel values

You can use transforms.Compose to chain these transformations together as follows:

import torchvision.transforms as transforms

transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.Grayscale(),
    transforms.Normalize(mean=[0.5], std=[0.5])
])

Then, you can apply this transform to your dataset using the `transform` argument in the dataset constructor.

from torchvision.datasets import ImageFolder

dataset = ImageFolder(root='path/to/dataset', transform=transform)

This will resize all the images in the dataset to 256x256, convert them to grayscale, and normalize the pixel values.