Fix nnx guide error

[divyashreepathihalli]

fixes this error in the guide : TypeError: got an unexpected keyword argument 'wrt'

[gemini-code-assist]

Summary of Changes

Hello @divyashreepathihalli, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request resolves a TypeError in the nnx guide by updating the nnx.grad function calls to align with the latest API. The change involves replacing the wrt keyword argument with argnums and utilizing nnx.DiffState for specifying differentiable arguments, ensuring the guide's code examples are accurate and executable.

Highlights

• nnx.grad API Update: The nnx.grad function call in the Keras NNX guide has been updated to use argnums=nnx.DiffState(...) instead of the deprecated wrt keyword argument, resolving a TypeError.
• Guide Code Correction: The code examples in the keras_nnx_guide.ipynb, keras_nnx_guide.py, and keras_nnx_guide.md files have been corrected to reflect the current nnx API for gradient computation.

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[gemini-code-assist]

Code Review

This pull request addresses a TypeError in the Keras NNX guide caused by a deprecated wrt argument in nnx.grad. The fix correctly updates the API usage to argnums with nnx.DiffState across the notebook, Python script, and markdown files. The changes are correct and resolve the issue. I've added a few suggestions to inline the nnx.DiffState creation for better code conciseness in the guide examples.

[gemini-code-assist]

For conciseness, you can inline the creation of nnx.DiffState directly into the nnx.grad call. This removes the need for a temporary variable and makes the code a single, expressive line.

Suggested change

	" diff_state = nnx.DiffState(0, trainable_var)\n",
	" grads = nnx.grad(loss_fn, argnums=diff_state)(model)\n",
	" grads = nnx.grad(loss_fn, argnums=nnx.DiffState(0, trainable_var))(model)\n",

[gemini-code-assist]

For conciseness, you can inline the creation of nnx.DiffState directly into the nnx.grad call. This removes the need for a temporary variable and makes the code a single, expressive line.

Suggested change

	diff_state = nnx.DiffState(0, trainable_var)
	grads = nnx.grad(loss_fn, argnums=diff_state)(model)
	grads = nnx.grad(loss_fn, argnums=nnx.DiffState(0, trainable_var))(model)

[gemini-code-assist]

For conciseness, you can inline the creation of nnx.DiffState directly into the nnx.grad call. This removes the need for a temporary variable and makes the code a single, expressive line. This can help keep code examples in the guide clean and focused.

Suggested change

	diff_state = nnx.DiffState(0, trainable_var)
	grads = nnx.grad(loss_fn, argnums=diff_state)(model)
	grads = nnx.grad(loss_fn, argnums=nnx.DiffState(0, trainable_var))(model)