MaxPool3d() in PyTorch

MaxPool3d can compute the 4D or 5D tensor of one or more values computed by 3D max pooling from the 4D or 5D tensor of one or more elements. The kernel size, stride, padding, and dilation arguments must be integers greater than or equal to 1, while the return_indices and ceil_mode arguments are boolean flags. By default, the tensor's requires_grad attribute is False and remains False after using MaxPool3d. When return_indices is False, MaxPool3d returns only the output tensor. When return_indices is True, it also returns a tensor containing the indices of the maximum values in the input tensor. Increasing the kernel size increases the receptive field and reduces the output size, while increasing the stride reduces the output size and introduces gaps in the receptive field. Padding can be used to add zeros around the input tensor to control the output size and avoid information loss. Dilation increases the spacing between elements in the kernel without changing its size, allowing for larger receptive fields without reducing the output size. Ceil mode ensures that the output size is always rounded up to the nearest integer, which can be useful for certain applications.