code: def test_datatype_type_check():
      assert DataType.is_string("string")
  
      assert DataType.is_integer(1)
      assert DataType.is_integer(np.int32(1))
      assert not DataType.is_integer(np.int64(1))
      # Note that isinstance(True, int) returns True
      assert not DataType.is_integer(True)
  
      assert DataType.is_long(1)
      assert DataType.is_long(np.int64(1))
      assert not DataType.is_long(np.int32(1))
  
      assert DataType.is_boolean(True)
      assert DataType.is_boolean(np.bool_(True))
      assert not DataType.is_boolean(1)
  
      assert DataType.is_double(1.0)
      assert DataType.is_double(np.float64(1.0))
      assert not DataType.is_double(np.float32(1.0))
  
      assert DataType.is_float(1.0)
      assert DataType.is_float(np.float32(1.0))
      assert not DataType.is_float(np.float64(1.0))
  
      assert DataType.is_datetime(datetime.date(2023, 6, 26))
      assert DataType.is_datetime(np.datetime64("2023-06-26 00:00:00"))
      assert not DataType.is_datetime("2023-06-26 00:00:00")
  
imports: import datetime
  import json
  import math
  import re
  import numpy
  import pandas
  import pyspark
  import pyspark.sql.types
  import pytest
  from scipy.sparse import csc_matrix, csr_matrix
  from mlflow.exceptions import MlflowException
  from mlflow.models.utils import _enforce_tensor_spec
  from mlflow.pyfunc import _parse_spark_datatype
  from mlflow.types import DataType
  from mlflow.types.schema import Array, ColSpec, Object, ParamSchema, ParamSpec, Property, Schema, TensorSpec
  from mlflow.types.utils import _get_tensor_shape, _infer_colspec_type, _infer_param_schema, _infer_schema, _validate_input_dictionary_contains_only_strings_and_lists_of_strings
label_desc: Use image node_id 28 for calling a global function with example usage: test_datatype_type_check() without return types