code: def make_graph(
      nodes: Sequence[NodeProto],
      name: str,
      inputs: Sequence[ValueInfoProto],
      outputs: Sequence[ValueInfoProto],
      initializer: Optional[Sequence[TensorProto]] = None,
      doc_string: Optional[str] = None,
      value_info: Optional[Sequence[ValueInfoProto]] = None,
      sparse_initializer: Optional[Sequence[SparseTensorProto]] = None,
  ) -> GraphProto:
      """Construct a GraphProto
  
      Arguments:
          nodes: list of NodeProto
          name (string): graph name
          inputs: list of ValueInfoProto
          outputs: list of ValueInfoProto
          initializer: list of TensorProto
          doc_string (string): graph documentation
          value_info: list of ValueInfoProto
          sparse_initializer: list of SparseTensorProto
      Returns:
          GraphProto
      """
      if initializer is None:
          initializer = []
      if sparse_initializer is None:
          sparse_initializer = []
      if value_info is None:
          value_info = []
      graph = GraphProto()
      graph.node.extend(nodes)
      graph.name = name
      graph.input.extend(inputs)
      graph.output.extend(outputs)
      graph.initializer.extend(initializer)
      graph.sparse_initializer.extend(sparse_initializer)
      graph.value_info.extend(value_info)
      if doc_string:
          graph.doc_string = doc_string
      return graph
  
imports: import collections.abc
  import numbers
  import struct
  from cmath import isnan
  from typing import Any, Callable, Dict, KeysView, List, Optional, Sequence, Tuple, TypeVar, Union, cast
  import google.protobuf.message
  import numpy
  from onnx import IR_VERSION, AttributeProto, FunctionProto, GraphProto, MapProto, ModelProto, NodeProto, OperatorSetIdProto, OptionalProto, SequenceProto, SparseTensorProto, TensorProto, TensorShapeProto, TrainingInfoProto, TypeProto, ValueInfoProto, defs, mapping
label_desc: Use image node_id 5 for calling a global function with example usage: make_graph(nodes, name, inputs, outputs, initializer, doc_string, value_info, sparse_initializer) and returns: graph