code: def test_schema_inference_on_basic_numpy(pandas_df_with_all_types):
      for col in pandas_df_with_all_types:
          data = pandas_df_with_all_types[col].to_numpy()
          schema = _infer_schema(data)
          assert schema == Schema(
              [TensorSpec(type=data.dtype, shape=(-1,))]
          )
  
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 22 for calling a global function with example usage: test_schema_inference_on_basic_numpy(pandas_df_with_all_types) without return types