[WebGPU EP] fixes bugs in NCHW version of instance norm operator

[prathikr]

The instance norm operator suffered from the following issues that this PR addresses:

1. If {2, 80, 2} is the tensor shape, then there are 320 numbers. {2, 80, 1} is the logical shape where each element is a vec2, so there are 320 numbers as well. The InstanceNorm<false> code path was not passing the logical shape into the shader generation function causing incorrect output.
2. The output_size was being divided by components which affects how many workers are dispatched. In the case of components=4, 75% of outputs for the InstanceNorm<false> code path were not updated and remained 0 causing correctness issues.
3. All the tests, including ones explicitly marked NCHW, were being run on the preferred data layout (NHWC).
4. Typos and some implicit typing was fixed as well.

P.S. Fixes pyannote model

[github-actions]

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[Copilot]

Pull Request Overview

This PR addresses several bugs in the NCHW implementation of the WebGPU instance normalization operator by propagating the correct logical shapes, fixing dispatch sizing, ensuring tests run in NCHW layout, and cleaning up typos and implicit typing.

• Add is_nhwc flag to DefaultWebGpuExecutionProvider and use it to enable NCHW support in shader generation.
• Extend CPU tests with two new WebGPU NCHW tests for instance normalization.
• Rename misspelled variables, correct the output size calculation, and adjust the channel scale/shift tensor shape in instance_norm.cc.

Reviewed Changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated no comments.

File	Description
test/util/include/default_providers.h	Added is_nhwc default parameter to WebGPU provider factory.
test/util/default_providers.cc	Implemented is_nhwc logic for preferred layout configuration.
test/providers/cpu/nn/instance_norm_op_test.cc	Added two WebGPU-backed NCHW instance norm tests under USE_WEBGPU.
core/providers/webgpu/nn/instance_norm.cc	Fixed typo (hight→height), removed incorrect component division in output_size, and updated channel scale/shift tensor shape.