pi = (cls == pred[:, 5]).nonzero().view(-1)

This line of code is using PyTorch's `nonzero()` function to find the indices of all elements in the tensor `pred[:, 5]` that are equal to `cls`.

Here's a breakdown of what's happening:

- `pred` is a tensor with shape `(batch_size, num_classes)`, where `num_classes` is the number of classes in the classification problem.
- `pred[:, 5]` selects the 5th column of `pred`, which contains the predicted probabilities for the 6th class.
- `cls` is an integer representing the ground truth class label for the current batch.
- `cls == pred[:, 5]` creates a boolean tensor of the same shape as `pred[:, 5]`, where each element is True if the corresponding element in `pred[:, 5]` is equal to `cls`, and False otherwise.
- `nonzero()` returns the indices of all non-zero elements in the boolean tensor (i.e., all elements that are True). The result is a tensor of shape `(num_matches, 1)`, where `num_matches` is the number of elements in `pred[:, 5]` that are equal to `cls`.
- `view(-1)` reshapes the tensor into a 1D tensor of length `num_matches`.

In other words, this line of code is finding all the predicted probabilities in `pred` that match the ground truth class label `cls`, and returning their indices. These indices can then be used to compute various metrics (e.g., accuracy, precision, recall) for the current batch.