code: def make_sparse_tensor(
      values: TensorProto, indices: TensorProto, dims: Sequence[int]
  ) -> SparseTensorProto:
      """Construct a SparseTensorProto
  
      Args:
          values (TensorProto): the values
          indices (TensorProto): the indices
          dims: the shape
  
      Returns:
          SparseTensorProto
      """
      sparse = SparseTensorProto()
      sparse.values.CopyFrom(values)
      sparse.indices.CopyFrom(indices)
      sparse.dims.extend(dims)
      return sparse
  
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, subbyte
label_desc: Use image node_id 17 for calling a global function with example usage: make_sparse_tensor(values, indices, dims) and returns: sparse