fix(serve): make torch an optional dependency - #6166

Installing sagemaker-serve (or the umbrella sagemaker, which depends on
it) pulled torch, and on Linux the ~2.9 GB CUDA/cuDNN/NCCL closure, even
for API-only use. sagemaker-core already declared torch an extra;
sagemaker-serve declared it required, so serve users paid regardless.

The import-time blocker was DEFAULT_SERIALIZERS_BY_FRAMEWORK in
serve/constants.py, which instantiated TorchTensorSerializer() at module
scope, running `from torch import Tensor` on any `import sagemaker.serve`.

Changes:

- Store serializer/deserializer classes (not instances) in
  DEFAULT_SERIALIZERS_BY_FRAMEWORK; instantiate on lookup. Removes the
  import-time torch dependency.
- Move torch>=2.0.0 to a `torch` extra in sagemaker-serve, matching
  sagemaker-core. Add a `torch` extra to the umbrella sagemaker package
  (sagemaker-serve[torch]) so `pip install sagemaker[torch]` works.
- Duck-type the tensor check in TorchTensorSerializer: serialization only
  needs the object's own detach()/numpy(), so it no longer imports torch
  at all and works whenever the caller holds a tensor.
- Make the in-process model server's torch import lazy with a CPU
  fallback, so importing it no longer requires torch.
- Fix the error messages that told serve users to install the wrong
  package: TorchTensorDeserializer, the Triton translator, and the ONNX
  export path now name sagemaker-serve[torch].
- Update and add tests asserting the whole surface imports and serializes
  without torch, and that the paths that genuinely construct torch
  objects (deserializer, Triton translator) still raise a clear error
  naming the extra.

Fixes aws#5531

Breaking change: users who installed plain sagemaker-serve and relied on
a torch code path (deserializing to tensor/pt, or ONNX/Triton export of a
PyTorch model) must now install sagemaker-serve[torch]. Both paths
inherently require a torch object the caller supplies, so torch is
already present in practice; a GitHub-wide search found no external
callers of these paths that do not already import torch.

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X-AI-Prompt: address the reviewer concern on whether making torch optional breaks existing customers; find ways to reduce the torch dependency and update pyproject and docs; consolidate all changes in the goelakash93 fork for a possible major version bump.
X-AI-Tool: Claude Code