code: def test_get_schema_type(dict_of_ndarrays):
      schema = _infer_schema(dict_of_ndarrays)
      assert ["float64"] * 4 == schema.numpy_types()
      with pytest.raises(
          MlflowException, match="TensorSpec only supports numpy types"
      ):
          schema.pandas_types()
      with pytest.raises(
          MlflowException,
          match="TensorSpec cannot be converted to spark dataframe",
      ):
          schema.as_spark_schema()
  
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 8 for calling a global function with example usage: test_get_schema_type(dict_of_ndarrays) without return types