[Chaitanya140904]

This PR adds support for the ONNX QLinearConv operator in the Relax ONNX frontend, resolving #20959.

Key Changes

• In python/tvm/relax/frontend/onnx/onnx_frontend.py:
  • Implemented QLinearConv op converter supporting opset 10.
  • Supports 1D, 2D, and 3D convolutions with dequantization of input activations and weights.
  • Supports per-channel and per-tensor scales and zero-points with channel broadcasting ([1, C, 1, 1] and [O, 1, 1, 1]).
  • Supports auto_pad modes (SAME_UPPER, SAME_LOWER, VALID, NOTSET), strides, dilations, and groups.
  • Handles quantized bias scaling (B * x_scale * w_scale).
  • Requantizes and clamps results to the output integer type range.
  • Registered "QLinearConv": QLinearConv in _get_convert_map().
• In tests/python/relax/test_frontend_onnx.py:
  • Added test_qlinearconv covering 2D convolutions with and without bias, and multi-channel convolutions with custom padding, validated against ONNX Runtime.

Closes #20959.

[Chaitanya140904]

Hi @mshr-h, @yzhliu, @tqchen, @akaashrp

Could you please take a look at this PR when you get a chance?