[FIX] is_contiguous returns True for empty tensors (numel() == 0)

[tqchen]

[FIX] is_contiguous returns True for empty tensors (numel() == 0)

An empty tensor — one where any shape dimension is 0 — has no elements
and its stride values carry no meaning. The previous implementation
iterated over strides and returned False when it encountered a stride
that did not match the expected row-major stride (e.g. strides all-zero
for a shape like (4, 0, 4)), which is incorrect.

This commit adds an early return when numel == 0 to both:

• C++: IsContiguous(const DLTensor&) in
  include/tvm/ffi/container/tensor.h (checks any shape dim == 0
  before the stride loop)
• Python/Cython: is_contiguous() in
  python/tvm_ffi/cython/tensor.pxi (same shape-zero scan before the
  stride loop)

Semantics now match NumPy and PyTorch: an empty tensor is trivially
contiguous.

Tests added:

• C++: TEST(Tensor, EmptyTensorIsContiguous) in
  tests/cpp/test_tensor.cc — constructs a strided empty tensor with
  strides (0,0,0) and asserts IsContiguous() == true.
• Python: test_empty_tensor_attributes in
  tests/python/test_tensor.py — existing test updated to use the
  correct numpy-reported strides (0,0,0) for shape (4,0,4) and now
  asserts is_contiguous() == True.

Closes #607 (comment)

[gemini-code-assist]

Code Review

This pull request ensures that empty tensors (where at least one dimension is 0) are treated as trivially contiguous in both C++ and Cython, matching NumPy and PyTorch semantics. It also adds a C++ test and updates a Python test to verify this behavior. There are no review comments, so I have no feedback to provide.

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[tqchen]

Folding this into #607. Closing in favor of that PR.