code: def compute_conv_transpose_padding_args_for_jax(
      input_shape,
      kernel_shape,
      strides,
      padding,
      output_padding,
      dilation_rate,
  ):
      num_spatial_dims = len(input_shape) - 2
      kernel_spatial_shape = kernel_shape[:-2]
  
      jax_padding = []
      for i in range(num_spatial_dims):
          output_padding_i = (
              output_padding
              if output_padding is None
              or isinstance(output_padding, int)
              else output_padding[i]
          )
          strides_i = (
              strides if isinstance(strides, int) else strides[i]
          )
          dilation_rate_i = (
              dilation_rate
              if isinstance(dilation_rate, int)
              else dilation_rate[i]
          )
          (
              pad_left,
              pad_right,
          ) = _convert_conv_tranpose_padding_args_from_keras_to_jax(
              kernel_size=kernel_spatial_shape[i],
              stride=strides_i,
              dilation_rate=dilation_rate_i,
              padding=padding,
              output_padding=output_padding_i,
          )
          jax_padding.append((pad_left, pad_right))
  
      return jax_padding
  
imports: import warnings
label_desc: Use image node_id 3 for calling a global function with example usage: compute_conv_transpose_padding_args_for_jax(input_shape, kernel_shape, strides, padding, output_padding, dilation_rate) and returns: jax_padding