code: def index_select_cat_fwd(
      output: torch.Tensor,
      source: torch.Tensor,
      index: torch.Tensor,
  ):
      if not (source.is_cuda and index.is_cuda):
          raise ValueError(
              "The index tensor and the source tensor must be of type CUDA!"
          )
  
      if not source.ndim == 2:
          raise ValueError(
              f"Expected 2-dimensional tensor, got {source.ndim}."
          )
      if not index.ndim == 1:
          raise ValueError(
              f"Expected 1-dimensional tensor, got {index.ndim}."
          )
  
      num_rows, num_cols = source.shape
      num_indices = index.shape[0]
  
      if not num_indices < num_rows:
          raise ValueError(
              "The number of indices cannot exceed the number of rows in the source matrix."
          )
  
      stride0, stride1 = source.stride(0), source.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_fwd_kernel[grid](
          output,
          source,
          index,
          num_indices,
          num_cols,
          stride0,
          stride1,
          BLOCK_SIZE_INDEX=1,
          BLOCK_SIZE_COL=512,
      )
  
      return output
  
imports: import torch
  import triton
  import triton.language
label_desc: Use image node_id 2 for calling a global function with example usage: index_select_cat_fwd(output, source, index) and returns: output, triton,  triton