no, i mean pytorch tensor stride

I see, sorry for the confusion earlier. In PyTorch, a tensor's stride is a tuple of integers representing the number of elements that need to be skipped in memory to go to the next element in each dimension. The stride values depend on the layout of the tensor in memory and are used to calculate the memory address of each element.

For example, consider a 2D tensor with shape (3, 4) stored in row-major order (default in PyTorch). This means that the elements in the same row are stored next to each other in memory. If we access tensor[1, 2], we need to skip 1 element in the first dimension and 2 elements in the second dimension to get to the desired element. The stride values for this tensor would be (4, 1), indicating that we need to skip 4 elements in memory to go to the next row and 1 element to go to the next column.

You can access the stride values of a PyTorch tensor using the `stride()` method. For example:

import torch

x = torch.randn(3, 4)
print(x.stride())  # prints (4, 1)