Implement vander function in keras.ops

[shashaka]

Adds keras.ops.vander, which constructs a Vandermonde matrix from a 1-D input tensor.
Supported across NumPy, TensorFlow, PyTorch, and JAX backends.
Not supported on OpenVINO.

[gemini-code-assist]

Summary of Changes

Hello @shashaka, 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 introduces the vander function to the keras.ops module, enabling the creation of Vandermonde matrices within the Keras ecosystem. This new operation is designed for multi-backend compatibility, with specific implementations for NumPy, TensorFlow, PyTorch, and JAX, ensuring robust and consistent numerical behavior. It also includes necessary input validation and proper handling for symbolic tensors, while explicitly noting its current unavailability for the OpenVINO backend.

Highlights

• New vander function: Introduced keras.ops.vander to construct Vandermonde matrices from 1-D input tensors.
• Multi-backend support: Implemented for NumPy, TensorFlow, PyTorch, and JAX backends, ensuring consistent behavior across frameworks.
• OpenVINO exclusion: Explicitly marked vander as not supported for the OpenVINO backend, with a NotImplementedError.
• Input validation: Added comprehensive validation for input tensor dimensions, N (number of columns), and increasing parameter.
• Symbolic tensor handling: Included a Vander operation class to correctly handle symbolic tensors and infer output shapes.
• Unit tests: Comprehensive tests were added to verify the correctness, shape inference, and dtype handling of the vander function across different scenarios.

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[gemini-code-assist]

Code Review

This pull request introduces the keras.ops.vander function, providing a consistent API for creating Vandermonde matrices across NumPy, JAX, PyTorch, and TensorFlow backends. The implementation is well-structured, with backend-specific logic and a unified public-facing operation.

I've identified a couple of areas for improvement. There are potential precision loss issues in the NumPy and TensorFlow backend implementations when handling float64 inputs, for which I've provided suggestions. Additionally, I've noted that the input validation logic is duplicated across all backends and could be centralized in the main vander op function to improve maintainability.

Overall, this is a solid contribution. The tests are comprehensive, covering shapes, correctness, and dtypes.

[codecov-commenter]

Codecov Report

❌ Patch coverage is 79.31034% with 12 lines in your changes missing coverage. Please review.
✅ Project coverage is 82.57%. Comparing base (f2c00fe) to head (ac0728d).

Files with missing lines	Patch %	Lines
keras/src/ops/numpy.py	64.28%	5 Missing and 5 partials ⚠️
keras/api/_tf_keras/keras/ops/__init__.py	0.00%	1 Missing ⚠️
keras/api/_tf_keras/keras/ops/numpy/__init__.py	0.00%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master   #21882      +/-   ##
==========================================
- Coverage   82.57%   82.57%   -0.01%     
==========================================
  Files         577      577              
  Lines       59599    59657      +58     
  Branches     9351     9360       +9     
==========================================
+ Hits        49213    49259      +46     
- Misses       7978     7985       +7     
- Partials     2408     2413       +5     

Flag	Coverage Δ
keras	82.38% <79.31%> (-0.01%)	⬇️
keras-jax	62.86% <44.82%> (-0.02%)	⬇️
keras-numpy	57.51% <50.00%> (-0.01%)	⬇️
keras-openvino	34.33% <32.75%> (-0.01%)	⬇️
keras-tensorflow	64.40% <60.34%> (-0.01%)	⬇️
keras-torch	63.56% <46.55%> (-0.02%)	⬇️

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