Let's look at an example of reshaping a tensor with einops. Assume we have a 4D shape tensor (batch_size, height, width, channels) representing a batch of RGB photos. We wish to reshape the tensor to have the shape (batch_size, channels, height * width) so that we can multiply the flattened image features with a matrix.

Without einops, we'd have to reshape the tensor using several lines of code, like this:

import torch

# create a tensor of shape (batch_size, height, width, channels)
x = torch.randn((32, 64, 64, 3))

# reshape the tensor to have shape (batch_size, channels, height * width)
x = x.permute(0, 3, 1, 2)  # move the channel dimension to the second axis
x = x.reshape((32, 3, 64 * 64))  # flatten the height and width dimensions

With einops, we can perform the same operation with a single line of code, like this:

from einops import rearrange

# create a tensor of shape (batch_size, height, width, channels)
x = torch.randn((32, 64, 64, 3))

# reshape the tensor to have shape (batch_size, channels, height * width)
x = rearrange(x, 'b h w c -> b c (h w)')

In einops, the rearrange function takes a tensor and a pattern string as input and rearranges the tensor according to the pattern string. The pattern string 'b h w c -> b c (h w)' in this example specifies that the tensor should be rearranged to have shape (batch_size, channels, height * width), where the first dimension of the input tensor remains unchanged, the third dimension becomes the output tensor's second dimension, and the second and fourth dimensions are flattened into a single dimension.

As you can see, employing einops makes reshaping tensors lot easier and more straightforward, especially when dealing with complex tensor operations.