code: def test_signature_inference_infers_datime_types_as_expected():
      col_name = "datetime_col"
      test_datetime = np.datetime64("2021-01-01")
      test_series = pd.Series(pd.to_datetime([test_datetime]))
      test_df = test_series.to_frame(col_name)
  
      signature = infer_signature(test_series)
      assert signature.inputs == Schema([ColSpec(DataType.datetime)])
  
      signature = infer_signature(test_df)
      assert signature.inputs == Schema(
          [ColSpec(DataType.datetime, name=col_name)]
      )
  
      with pyspark.sql.SparkSession.builder.getOrCreate() as spark:
          spark_df = spark.range(1).selectExpr(
              "current_timestamp() as timestamp",
              "current_date() as date",
          )
          signature = infer_signature(spark_df)
          assert signature.inputs == Schema(
              [
                  ColSpec(DataType.datetime, name="timestamp"),
                  ColSpec(DataType.datetime, name="date"),
              ]
          )
  
imports: import json
  import numpy
  import pandas
  import pyspark
  import pytest
  from sklearn.ensemble import RandomForestRegressor
  import mlflow
  from mlflow.exceptions import MlflowException
  from mlflow.models import Model, ModelSignature, infer_signature, set_signature
  from mlflow.models.model import get_model_info
  from mlflow.types import DataType
  from mlflow.types.schema import Array, ColSpec, ParamSchema, ParamSpec, Schema, TensorSpec
label_desc: Use image node_id 7 for calling a global function with example usage: test_signature_inference_infers_datime_types_as_expected() without return types