code: def get_segment_details(
      model: Any,
      scorer: Callable,
      dataset: tabular.Dataset,
      segment_condition_col: pd.Series,
  ) -> Tuple[str, Dict[str, float]]:
      """Return details about the data segment using the scorer and model."""
      performance = scorer(
          model,
          dataset.copy(dataset.data[segment_condition_col.values]),
      )
      n_samples = dataset.data[segment_condition_col].shape[0]
      segment_label = (
          f"{scorer.name}: {format_number(performance)}, "
          f"Samples: {n_samples} ({format_percent(n_samples / len(dataset))})"
      )
  
      segment_details = {
          "score": performance,
          "n_samples": n_samples,
          "frac_samples": n_samples / len(dataset),
      }
  
      return segment_label, segment_details
  
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 5 for calling a global function with example usage: get_segment_details(model, scorer, dataset, segment_condition_col) and returns: segment_label,  segment_details