code: def onnx_conv(
      x,
      w,
      b=0,
      group=1,
      kernel_shape=None,
      pads=None,
      strides=None,
      dilations=None,
      auto_pad=None,
  ):
      """Numpy-backed implementation of ONNX Conv op."""
      assert group == 1
      kernel_shape = kernel_shape or w.shape
      strides = strides or [1] * (w.ndim - 2)
      if auto_pad:
          auto_pad = "SAME" if auto_pad.startswith(b"SAME") else "VALID"
          pads = lax.padtype_to_pads(
              x.shape[2:], w.shape[2:], strides, auto_pad
          )
      else:
          pads = pads or [0] * (w.ndim - 2)
      lhs_dilation = [1] * (w.ndim - 2)
      rhs_dilation = dilations or [1] * (w.ndim - 2)
      return [
          lax.conv_with_general_padding(
              x, w, strides, pads, lhs_dilation, rhs_dilation
          )
          + b
      ]
  
file_path: jax/examples/onnx2xla.py
imports: from io import BytesIO
  import hashlib
  import urllib.request
  import sys
  import numpy
  import onnx
  from onnx import numpy_helper
  import jax.numpy
  from jax import jit, grad
  from jax import lax
label_desc: Use image node_id 3 for calling a global function with example usage: onnx_conv(x, w, b, group, kernel_shape, pads, strides, dilations, auto_pad) and returns: list