code: def setup_pyspark(
      self,
      model,
      dataset,
      true_y,
      classes,
      features,
      categorical_features,
      true_y_dataset,
      pred_y,
      pred_y_dataset,
      model_task,
      metric,
      max_depth,
      num_leaves,
      min_child_samples,
      sample_dataset,
      model_available,
  ):
      self._error_analyzer = ModelAnalyzer(
          model,
          dataset,
          true_y,
          features,
          categorical_features,
          model_task,
          metric,
          classes,
      )
      sample = dataset.to_spark().limit(100)
      scored_sample = model.transform(sample)
      pd_sample = scored_sample.toPandas()
      predicted_y = pd_sample["prediction"]
      predicted_y = self.predicted_y_to_list(predicted_y)
      true_y = pd_sample[true_y]
      pd_sample = pd_sample[features]
      list_dataset = convert_to_list(pd_sample)
      self.setup_visualization_input(
          classes, predicted_y, list_dataset, true_y, 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 2 for calling the ErrorAnalysisDashboardInput obj's underlying member method code with example usage: obj.setup_pyspark(model, dataset, true_y, classes, features, categorical_features, true_y_dataset, pred_y, pred_y_dataset, model_task, metric, max_depth, num_leaves, min_child_samples, sample_dataset, model_available) without return types