code: def main(_):
      buffers_to_skip = FLAGS.buffers_to_skip
      ops_to_skip = [op.upper() for op in FLAGS.ops_to_skip]
      ops_operands_to_skip = {}
      for op_operands_to_skip in FLAGS.ops_operands_to_skip:
          op_name, indices = op_operands_to_skip.split(":")
          op_name_upper = op_name.upper()
          if op_name_upper in ops_operands_to_skip:
              raise ValueError(
                  "Indices for the same op must be specified only once multiple"
                  f" specification for op {op_name}."
              )
          ops_operands_to_skip[op_name_upper] = list(
              map(int, indices.split(","))
          )
  
      model = flatbuffer_utils.read_model(FLAGS.input_tflite_file)
  
      # Add in buffers for ops in ops_to_skip or ops_operands_to_skip to the list of
      # skipped buffers.
      for graph in model.subgraphs:
          for op in graph.operators:
              op_name = flatbuffer_utils.opcode_to_name(
                  model, op.opcodeIndex
              )
              op_name_upper = op_name.upper()
              if op_name_upper in ops_to_skip:
                  for input_idx in op.inputs:
                      buffers_to_skip.append(
                          graph.tensors[input_idx].buffer
                      )
              if op_name_upper in ops_operands_to_skip:
                  for operand_idx in ops_operands_to_skip[
                      op_name_upper
                  ]:
                      buffers_to_skip.append(
                          graph.tensors[op.inputs[operand_idx]].buffer
                      )
  
      flatbuffer_utils.randomize_weights(
          model, FLAGS.random_seed, FLAGS.buffers_to_skip
      )
      flatbuffer_utils.write_model(model, FLAGS.output_tflite_file)
  
imports: from absl import app
  from absl import flags
  from tensorflow.lite.tools import flatbuffer_utils
label_desc: Use image node_id 1 for calling a global function with example usage: main(_) without return types