code: def create_random_int(
      input_shape: Tuple[int], dtype: np.dtype, seed: int = 1
  ) -> np.ndarray:
      """Create random integer array for backend/test/case/node.
  
      Args:
          input_shape: The shape for the returned integer array.
          dtype: The NumPy data type for the returned integer array.
          seed: The seed for np.random.
  
      Returns:
          np.ndarray: Random integer array.
      """
      np.random.seed(seed)
      if dtype in (
          np.uint8,
          np.uint16,
          np.uint32,
          np.uint64,
          np.int8,
          np.int16,
          np.int32,
          np.int64,
      ):
          # the range of np.random.randint is int32; set a fixed boundary if overflow
          end = min(np.iinfo(dtype).max, np.iinfo(np.int32).max)
          start = max(np.iinfo(dtype).min, np.iinfo(np.int32).min)
          return np.random.randint(start, end, size=input_shape).astype(
              dtype
          )
      else:
          raise TypeError(
              f"{dtype} is not supported by create_random_int."
          )
  
imports: import sys
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  from onnx import MapProto, OptionalProto, SequenceProto, TensorProto, helper, subbyte
  from onnx.external_data_helper import load_external_data_for_tensor, uses_external_data
label_desc: Use image node_id 17 for calling a global function with example usage: create_random_int(input_shape, dtype, seed) and returns: np