code: def parse_transpose_layer(
      reader, node, inputs_map, input_shapes, graph, config
  ):
      layer = {}
      layer["name"] = node.name
      layer["class_name"] = "Transpose"
      layer["inputs"] = get_onnx_input_name(node, graph)
  
      perm = [list(i.ints) for i in node.attribute][
          0
      ]  # This will get something like [[a,b,c]][0] = [a,b,c]
      layer["perm"] = [
          x - 1 for x in perm[1:]
      ]  # Ignore the batch dimension in ONNX, and adjust the perm indexing
  
      output_shape = [input_shapes[0][i] for i in perm]
  
      return layer, output_shape
  
label_desc: Use image node_id 1 for calling a global function with example usage: parse_transpose_layer(reader, node, inputs_map, input_shapes, graph, config) and returns: layer,  output_shape