code: def setup_visualization_input(
      self, classes, predicted_y, list_dataset, true_y, features
  ):
      if classes is not None:
          classes = convert_to_list(classes)
          self.dashboard_input[
              ExplanationDashboardInterface.CLASS_NAMES
          ] = classes
          class_to_index = {k: v for v, k in enumerate(classes)}
  
      if predicted_y is not None:
          # If classes specified, convert predicted_y to
          # numeric representation
          if classes is not None and predicted_y[0] in class_to_index:
              for i in range(len(predicted_y)):
                  predicted_y[i] = class_to_index[predicted_y[i]]
          self.dashboard_input[
              ExplanationDashboardInterface.PREDICTED_Y
          ] = predicted_y
  
      row_length = 0
      feature_length = None
  
      if list_dataset is not None:
          row_length, feature_length = np.shape(list_dataset)
          if feature_length > 1000:
              raise ValueError(
                  "Exceeds maximum number of features for"
                  " visualization (1000). Please regenerate the"
                  " explanation using fewer features or"
                  " initialize the dashboard without passing a"
                  " dataset."
              )
          self.dashboard_input[
              ExplanationDashboardInterface.TRAINING_DATA
          ] = serialize_json_safe(list_dataset)
  
      if true_y is not None and len(true_y) == row_length:
          list_true_y = convert_to_list(true_y)
          # If classes specified, convert true_y to numeric representation
          if classes is not None and list_true_y[0] in class_to_index:
              for i in range(len(list_true_y)):
                  list_true_y[i] = class_to_index[list_true_y[i]]
          self.dashboard_input[
              ExplanationDashboardInterface.TRUE_Y
          ] = list_true_y
  
      if features is not None:
          features = convert_to_list(features)
          if (
              feature_length is not None
              and len(features) != feature_length
          ):
              raise ValueError(
                  "Feature vector length mismatch:"
                  " feature names length differs"
                  " from local explanations dimension"
              )
          self.dashboard_input[FEATURE_NAMES] = features
  
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 3 for calling the ErrorAnalysisDashboardInput obj's underlying member method code with example usage: obj.setup_visualization_input(classes, predicted_y, list_dataset, true_y, features) without return types