code: def error_model_display_dataframe(
      error_fi: pd.Series,
      error_model_predicted: pd.Series,
      dataset: pd.DataFrame,
      cat_features: List,
      max_features_to_show: int,
      min_feature_contribution: float,
      n_display_samples: int,
      min_segment_size: float,
      random_state: int,
      with_display: bool,
  ):
      """Wrap dataframe with tabular.Dataset for error_model_display with no scorer."""
      return error_model_display(
          error_fi,
          error_model_predicted,
          tabular.Dataset(dataset, cat_features=cat_features),
          None,
          None,
          max_features_to_show,
          min_feature_contribution,
          n_display_samples,
          min_segment_size,
          random_state,
          with_display,
      )
  
imports: from typing import Any, Callable, Dict, Hashable, List, Optional, Tuple
  import numpy
  import pandas
  import plotly.express
  from category_encoders import TargetEncoder
  from sklearn.compose import ColumnTransformer
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.impute import SimpleImputer
  from sklearn.metrics import r2_score
  from sklearn.pipeline import Pipeline
  from sklearn.tree import DecisionTreeRegressor
  from deepchecks import tabular
  from deepchecks.core.errors import DeepchecksProcessError
  from deepchecks.tabular import Dataset
  from deepchecks.tabular.utils.feature_importance import _calculate_feature_importance
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.utils.plot import colors
  from deepchecks.utils.strings import format_number, format_percent
label_desc: Use image node_id 3 for calling a global function with example usage: error_model_display_dataframe(error_fi, error_model_predicted, dataset, cat_features, max_features_to_show, min_feature_contribution, n_display_samples, min_segment_size, random_state, with_display) and returns: error_model_display