gemini-code-assist[bot] commented on PR #18658:
URL: https://github.com/apache/tvm/pull/18658#issuecomment-3737491054

   ## Summary of Changes
   
   Hello @locnd182644, I'm Gemini Code Assist[^1]! 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 significantly improves the ONNX frontend's capability to 
handle the PReLu operator by introducing robust logic for interpreting and 
processing various `slope` tensor shapes. The changes ensure that the `axis` 
argument is correctly inferred and the `slope` is appropriately reshaped, 
aligning with ONNX broadcasting rules. This enhancement prevents errors and 
increases the compatibility of Relax with ONNX models that utilize diverse 
PReLu configurations, ultimately making the frontend more flexible and reliable.
   
   ### Highlights
   
   * **Enhanced PReLu Operator Handling**: The ONNX frontend now supports a 
wider variety of `slope` tensor shapes for the PReLu operator, including 
`(1xCx1x1)`, `(S,)`, and `(1,1)`, ensuring correct interpretation and 
application of the slope argument.
   * **Dynamic Axis Determination**: Introduced logic within the `PRelu` 
converter to dynamically determine the correct `axis` argument for the 
`relax.op.nn.prelu` function based on the input `x` shape and the `slope` 
tensor's dimensions.
   * **Robust Slope Reshaping**: Implemented conditional reshaping of the 
`slope` tensor to a 1D array before passing it to the `relax.op.nn.prelu` 
function, accommodating different broadcasting patterns defined by ONNX.
   * **Improved `topi.nn.elemwise.prelu`**: Modified the `prelu` function in 
`tvm.topi.nn.elemwise` to conditionally broadcast the slope only when its first 
dimension is 1, optimizing behavior for already correctly shaped slopes.
   * **Expanded Test Coverage**: Added new test cases for the `PRelu` operator 
in `test_frontend_onnx.py` to validate the correct handling of the newly 
supported `slope` shapes, ensuring robustness and preventing regressions.
   
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