[DOCS ]Add annotated partitioning documentation (#27972)
This pull request introduces a new documentation page,
`PartitioningWithAnnotationsAndMemoryConstraints.md`, which explains
advanced ONNX Runtime features for partitioning model graphs across
devices with explicit control. The doc covers how to annotate model
layers for device assignment, collect per-node memory statistics, and
enforce GPU memory budgets during partitioning. These features enable
precise control over device placement and memory usage for large models.
The most important changes are:
**New Documentation: Advanced Partitioning Features**
* Adds a comprehensive guide
(`PartitioningWithAnnotationsAndMemoryConstraints.md`) describing how to
use ONNX Runtime’s layer annotation and memory constraint features for
graph partitioning.
**Layer Assignment via Annotations**
* Explains how to annotate ONNX model nodes with `layer_ann` metadata,
including manual annotation and automated annotation using Olive’s
`CaptureLayerAnnotations` pass.
* Provides configuration examples for mapping annotation patterns to
devices at runtime using the `session.layer_assignment_settings` session
option.
**Capacity-Aware Partitioning**
* Details a two-phase workflow for profiling per-node memory usage and
then enforcing a memory budget with the
`session.resource_cuda_partitioning_settings` session option.
* Covers both profiling-based and ad-hoc (estimation-only) approaches
for memory-constrained partitioning.
([docs/annotated_partitioning/PartitioningWithAnnotationsAndMemoryConstraints.mdR1-R267](diffhunk://#diff-10b3051b9e36eccfc7ca0f2d44ce78a9980ca573cde0f931ffd1456da2c681daR1-R267)
This is a follow up for
https://github.com/microsoft/onnxruntime/pull/27595
