code: def create_tensor_data(dtype, shape, min_value=-100, max_value=100):
      """Build tensor data spreading the range [min_value, max_value)."""
  
      if dtype in MAP_TF_TO_NUMPY_TYPE:
          dtype = MAP_TF_TO_NUMPY_TYPE[dtype]
  
      if dtype in (tf.float32, tf.float16, tf.float64):
          value = (max_value - min_value) * np.random.random_sample(
              shape
          ) + min_value
      elif dtype in (tf.complex64, tf.complex128):
          real = (max_value - min_value) * np.random.random_sample(
              shape
          ) + min_value
          imag = (max_value - min_value) * np.random.random_sample(
              shape
          ) + min_value
          value = real + imag * 1j
      elif dtype in (
          tf.uint32,
          tf.int32,
          tf.uint8,
          tf.int8,
          tf.int64,
          tf.uint16,
          tf.int16,
      ):
          value = np.random.randint(min_value, max_value + 1, shape)
      elif dtype == tf.bool:
          value = np.random.choice([True, False], size=shape)
      elif dtype == np.string_:
          # Not the best strings, but they will do for some basic testing.
          letters = list(string.ascii_uppercase)
          return np.random.choice(letters, size=shape).astype(dtype)
      return (
          np.dtype(dtype).type(value)
          if np.isscalar(value)
          else value.astype(dtype)
      )
  
imports: import functools
  import io
  import itertools
  import operator
  import os
  import re
  import string
  import sys
  import tempfile
  import traceback
  import zipfile
  import numpy
  import tensorflow
  from google.protobuf import text_format
  from tensorflow.lite.testing import _pywrap_string_util
  from tensorflow.lite.testing import generate_examples_report
  from tensorflow.lite.tools import flatbuffer_utils
  from tensorflow.python.framework import convert_to_constants
  from tensorflow.python.saved_model import signature_constants
label_desc: Use image node_id 3 for calling a global function with example usage: create_tensor_data(dtype, shape, min_value, max_value) and returns: unknown, np