fix(serve): make torch an optional dependency

[goelakash]

Fixes #5531

sagemaker-serve lists torch>=2.0.0 as a required dependency, so any install of sagemaker, sagemaker-serve, or sagemaker-mlops pulls torch (and on Linux, the CUDA/cuDNN/NCCL stack) even for API-only use. sagemaker-core already treats torch as an extra.

The blocker was DEFAULT_SERIALIZERS_BY_FRAMEWORK in serve/constants.py, which instantiated TorchTensorSerializer() at module scope. That runs from torch import Tensor on any import sagemaker.serve. Every other torch reference in the package is already lazy.

Changes

• Store serializer/deserializer classes in DEFAULT_SERIALIZERS_BY_FRAMEWORK; instantiate at lookup in _fetch_serializer_and_deserializer_for_framework.
• Move torch>=2.0.0 to a torch extra, matching sagemaker-core.
• Update the two docstring examples and the affected unit tests.
• Add tests/unit/test_optional_torch_dependency.py, mirroring the existing sagemaker-core subprocess pattern.

Installing with the torch extra is unchanged. TorchTensorSerializer() still raises the same ImportError if torch is missing when actually used.

Testing

• tests/unit in a venv with and without torch: no new failures vs. master (53 pre-existing failures in both).
• New tests fail on master and pass with this change.
• Verified in a container with no torch installed: from sagemaker.serve import ModelBuilder and import sagemaker.mlops both succeed.
• Image size for all four packages on Amazon Linux 2023: 1.45 GB -> 536 MB.