code: def input_explanation(self, explanation, dataset, true_y):
      self._mli_explanations = explanation.data(-1)["mli"]
      dataset_explanation = self._find_first_explanation(
          ExplanationDashboardInterface.MLI_EXPLANATION_DATASET_KEY
      )
      if hasattr(explanation, "method"):
          self.dashboard_input[
              ExplanationDashboardInterface.EXPLANATION_METHOD
          ] = explanation.method
      if dataset_explanation is not None:
          if dataset is None or len(dataset) != len(true_y):
              dataset = dataset_explanation[
                  ExplanationDashboardInterface.MLI_DATASET_X_KEY
              ]
          if true_y is None:
              true_y = dataset_explanation[
                  ExplanationDashboardInterface.MLI_DATASET_Y_KEY
              ]
      elif len(dataset) != len(true_y):
          dataset = explanation._eval_data
      return dataset, true_y
  
imports: import traceback
  import numpy
  import pandas
  from erroranalysis._internal.constants import Metrics, display_name_to_metric, metric_to_display_name
  from erroranalysis._internal.error_analyzer import ModelAnalyzer, PredictionsAnalyzer
  from erroranalysis._internal.utils import is_spark
  from raiutils.data_processing import convert_to_list, serialize_json_safe
  from raiutils.models import is_classifier
  from responsibleai._interfaces import ErrorAnalysisData
  from .constants import ModelTask
  from .error_analysis_constants import ErrorAnalysisDashboardInterface, MethodConstants
  from .error_handling import _format_exception
  from .explanation_constants import ExplanationDashboardInterface, WidgetRequestResponseConstants
label_desc: Use image node_id 8 for calling the ErrorAnalysisDashboardInput obj's underlying member method code with example usage: obj.input_explanation(explanation, dataset, true_y) and returns: dataset,  true_y