`Tensor` indexing at Matrix parity

Problem

Tensor without a real subscript story is effectively write-only: a user can build one, do arithmetic on it, and aggregate it, but the moment they want "the first three rows" or "channel 0 of every frame" they have to drop back to constructing a fresh tensor and copying by hand. Matrix already accepts a sensible subset of NumPy keys; Tensor needs the same vocabulary lifted from rank 2 to arbitrary rank.

Desired functionality

Tensor.__getitem__ and Tensor.__setitem__ accept the same key shapes Matrix accepts today — int, slice, and tuples of those whose length is at most the tensor's rank — and behave the same way per axis. __getitem__ returns a new contiguous tensor of the appropriate shape. __setitem__ accepts either a scalar (broadcast to every targeted element) or a tensor whose shape exactly matches the indexed region.

Constraints

• Results are copies. No views, no aliasing of the original storage.
• Supported keys exhaust to: int, slice, tuple of (int | slice). Anything else raises TypeError naming the unsupported form.
• Negative integer indices wrap; out-of-range raises IndexError.
• __setitem__ does not invoke broadcasting on its right-hand side in v1: scalar broadcast yes, exact-shape Tensor yes, anything else TypeError.
• Matrix is untouched by this work.

Out of scope

Ellipsis, None / np.newaxis, boolean masks, integer-array and list-of-indices fancy indexing, value-broadcast on __setitem__, views.

Open questions

• Rank reduction. NumPy-style — tensor[i, :] on a rank-2 tensor returns rank 1 — versus Matrix-style — the same key returns rank 2 with an axis of size 1. The two are incompatible; one must be picked and documented. NumPy-style is the more conventional choice, but the project's existing rank-2 behaviour is the other one.
• Empty-result slices: raise IndexError (current Matrix behaviour) or return a zero-size tensor.
• Whether a rank-0 tensor accepts tensor[()] as identity.

Start by reading the existing Matrix indexing behavior and the Tensor shape and storage entry points. Resolve the open questions about rank reduction, empty slices, and rank-0 indexing before implementing Tensor.getitem and Tensor.setitem. Done means supported integer and slice keys work across arbitrary ranks, copies remain contiguous and unaliased, assignment accepts only scalars or exact-shape tensors, and unsupported forms raise the specified errors.