code: def index_select_cat_bwd(
      grad_source: torch.Tensor,
      index: torch.Tensor,
      grad_output: torch.Tensor,
  ):
      if not (grad_source.is_cuda and grad_output.is_cuda):
          raise ValueError(
              "The grad_source and grad_output tensor must be of type CUDA!"
          )
  
      if not (grad_source.ndim == 2 and grad_output.ndim == 2):
          raise ValueError(
              f"The grad_source and grad_output must be three-dimensional "
              f"(got {grad_source.ndim} and {grad_output.ndim})!"
          )
      if not grad_source.shape[1] == grad_output.shape[1]:
          raise ValueError(
              f"The number of elements along dimension 1 of grad_source and grad_output must be the same "
              f"(got {grad_source.shape[1]} and {grad_output.shape[1]})"
          )
  
      num_rows, num_cols = grad_source.shape
      num_indices, num_cols = grad_output.shape
      if not num_rows >= num_indices:
          raise ValueError(
              f"The number of elements along dimension 0 of grad_source must be larger than that of grad_output "
              f"(got {num_rows} and {num_indices})!"
          )
      if not index.shape[0] == num_indices:
          raise ValueError(
              f"The number of indices and the number of elements along dimension 0 of grad_output must match "
              f"(got {index.shape[0]} and {num_indices})!"
          )
  
      stride0, stride1 = grad_source.stride(0), grad_source.stride(1)
      if not (
          grad_output.stride(0) == stride0
          and grad_output.stride(1) == stride1
      ):
          raise ValueError(
              f"The strides of the grad_source and grad_output tensors must match "
              f"(got {stride0} vs. {grad_output.stride(0)}, {stride1} vs. {grad_output.stride(1)})!"
          )
  
      def grid(meta):
          return (
              triton.cdiv(num_indices, meta["BLOCK_SIZE_INDEX"]),
              triton.cdiv(num_cols, meta["BLOCK_SIZE_COL"]),
          )
  
      index_select_cat_bwd_kernel[grid](
          grad_source,
          index,
          grad_output,
          num_rows,
          num_indices,
          num_cols,
          grad_source.stride(0),
          grad_source.stride(1),
          BLOCK_SIZE_INDEX=1,
          BLOCK_SIZE_COL=512,
      )
  
      return
  
imports: import torch
  import triton
  import triton.language
label_desc: Use image node_id 4 for calling a global function with example usage: index_select_cat_bwd(grad_source, index, grad_output) and returns: triton,  triton