class_docstr: Unit test for Smart Data Lake Result Validation
class_docstr_tok: ["Unit","test","for","Smart","Data","Lake","Result","Validation"]
code: def test_result_is_of_dict_type_and_valid(self, context, logger):
      # Test Flow : Code Execution Successful with no exceptions
      result_validation = ResultValidation()
      output_type_helper = Mock()
  
      context._query_exec_tracker = Mock()
      context.query_exec_tracker.get_execution_time = Mock()
      context.get_intermediate_value = Mock(
          return_value=output_type_helper
      )
      output_type_helper.validate = Mock(
          return_value=(True, "Mocked Logs")
      )
  
      result = result_validation.execute(
          input={"Mocked": "Result"}, context=context, logger=logger
      )
  
      context.query_exec_tracker.add_step.assert_called_with(
          {
              "type": "Validating Output",
              "success": True,
              "message": "Output Validation Successful",
          }
      )
      assert isinstance(result_validation, ResultValidation)
      assert result == {"Mocked": "Result"}
  
imports: from typing import Optional
  from unittest.mock import Mock
  import pandas
  import pytest
  from pandasai.helpers.logger import Logger
  from pandasai.llm.fake import FakeLLM
  from pandasai.pipelines.pipeline_context import PipelineContext
  from pandasai.smart_dataframe import SmartDataframe
  from pandasai.pipelines.smart_datalake_chat.result_validation import ResultValidation
label_desc: Use image node_id 10 for calling the TestResultValidation obj's underlying member method code with example usage: obj.test_result_is_of_dict_type_and_valid(context, logger) without return types