onnxruntime

[TransposeOptimizer] Support Unsqueeze/Transpose of input consumed by per-axis DQ (#21821)

### Description
Follow-up to: https://github.com/microsoft/onnxruntime/pull/21793

- Support looking past a per-axis DQ to do in-place Unsqueeze/Transpose
of initializers
- Support looking past a per-axis DQ to cancel a Transpose or Squeeze.

### Test models
For all test models, the transpose optimizer pushes a Transpose through
a Mul's input[0]. The Mul's input[1] is optionally unsqueezed and then
transposed.

### I. Test in-place unsqueeze and transpose of per-axis quantized
weight
Original model has input[1] with shape (3,)
<details><summary>click to expand model image</summary>
<img
src="https://github.com/user-attachments/assets/37b6f60c-77d2-4bd3-8ca2-58dc7c88a304"
/>
</details>

Optimized model has input[1] with shape (1, 3, 1, 1). The initializer
was unsqueezed and transposed in-place.
<details><summary>click expand model image</summary>
<img
src="https://github.com/user-attachments/assets/adb72757-a164-400c-bfef-2a05f0e35825"
/>
</details>

### II. Test canceling existing Squeeze before per-axis DQ
Original model has input[1] that is squeezed.
<details><summary>click expand model image</summary>
<img
src="https://github.com/user-attachments/assets/f27e6742-b563-42a9-ad06-bb3178b0ceb8"
/>
</details>

Optimized model unsqueezed and transposed input[1]. The original squeeze
was removed due to the unsqueeze, leaving only the Transpose.
<details><summary>click expand model image</summary>
<img
src="https://github.com/user-attachments/assets/e56261d4-eba6-4a9f-847b-dcd33548dd07"
/>
</details>

### III. Test canceling existing Transpose before per-axis DQ
Original model has input[1] that is transposed.
<details><summary>click expand model image</summary>
<img
src="https://github.com/user-attachments/assets/f157e04a-572a-479d-8e3b-cf57954df5c0"
/>
</details>

Optimized model transposed input[1], thus canceling the existing
transpose.
<details><summary>click expand model image</summary>
<img
src="https://github.com/user-attachments/assets/63d742ce-3762-4ab2-bdb0-1b507886da9d"
/>
</details>

### IV. Test QDQ fix-up of Transpose/Unsqueeze for per-axis quantization
Original model has input[1] that can be broadcasted.
<details><summary>click expand model image</summary>
<img
src="https://github.com/user-attachments/assets/96c0092c-22ec-486d-882e-e2cb59ffe324"
/>
</details>

The main transpose optimization loop inserts float32 Unsqueeze and
Transpose after the DQ. The qdq fix-up pass inserts new per-axis Q/DQ
ops after the inserted nodes.
<details><summary>click expand model image</summary>
<img
src="https://github.com/user-attachments/assets/b6f89c11-974d-4b35-922f-11effdf06883"
/>
</details>


### Motivation and Context
Enables the TransposeOptimizer to support more models with per-axis QDQ
nodes. Per-axis quantization can improve model accuracy and is used by
EPs like QNN.

---------

Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>