How does NumPy handle internal memory views for reshaped tensors?

Reshape creates a view when memory layout is compatible, otherwise it creates a copy.

Reshaping relies on stride reinterpretation. If the array is contiguous in memory, reshape only updates metadata like shape and strides. If the layout is non-contiguous, NumPy must allocate a new buffer and copy data to maintain logical correctness.