code: def get_error_analysis_data(
      self, max_depth, num_leaves, min_child_samples, metric, is_pyspark
  ):
      data = ErrorAnalysisData()
      data.maxDepth = max_depth
      data.numLeaves = num_leaves
      data.minChildSamples = min_child_samples
      data.metric = metric_to_display_name[metric]
      if not is_pyspark:
          data.root_stats = self._error_analyzer.compute_root_stats()
      else:
          features = self.dashboard_input[FEATURE_NAMES]
          filters = []
          composite_filters = []
          self._error_analyzer.update_metric(metric)
          tree = self._error_analyzer.compute_error_tree(
              features,
              filters,
              composite_filters,
              max_depth=max_depth,
              num_leaves=num_leaves,
              min_child_samples=min_child_samples,
          )
          data.tree = tree
          data.tree_features = features
      return data
  
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 5 for calling the ErrorAnalysisDashboardInput obj's underlying member method code with example usage: obj.get_error_analysis_data(max_depth, num_leaves, min_child_samples, metric, is_pyspark) and returns: data