code: def test_load_from_date_partitioned_s3(
      input_loader, spark_session, mock_data_frame, condition
  ):
      input_loader._get_s3_partitions_offset_filter_condition = Mock(
          return_value=condition
      )
  
      input_loader.load_from_date_partitioned_s3(
          ParquetDataSource("s3://path/to/file"), "start", "end"
      )
      df = spark_session.read.parquet
      df.assert_called_with("s3a://path/to/file")
  
      if condition:
          mock_data_frame.filter.assert_called_with(condition)
      else:
          mock_data_frame.filter.assert_not_called()
  
imports: from __future__ import absolute_import
  import pytest
  import test_data_helpers
  from mock import Mock, patch, call
  from pyspark.sql import SparkSession, DataFrame
  from sagemaker.feature_store.feature_processor._data_source import CSVDataSource, FeatureGroupDataSource, ParquetDataSource, IcebergTableDataSource
  from sagemaker.feature_store.feature_processor._input_loader import SparkDataFrameInputLoader
  from sagemaker.feature_store.feature_processor._spark_factory import SparkSessionFactory
  from sagemaker.feature_store.feature_processor._env import EnvironmentHelper
  from sagemaker.session import Session
label_desc: Use image node_id 18 for calling a global function with example usage: test_load_from_date_partitioned_s3(input_loader, spark_session, mock_data_frame, condition) without return types