code: def _warn_n_top_columns(data_type: str, n_top_features: int):
      """Warn if n_top_columns is smaller than the number of segmenting features (metadata or properties)."""
      if data_type == "metadata":
          features_name = "metadata columns"
          n_top_columns_parameter = "n_top_columns"
          columns_parameter = "columns"
      else:
          features_name = "properties"
          n_top_columns_parameter = "n_top_properties"
          columns_parameter = "properties"
  
      warnings.warn(
          f"Parameter {n_top_columns_parameter} is set to {n_top_features} to avoid long computation time. "
          f"This means that the check will run on {n_top_features} {features_name} selected at random. "
          f"If you want to run on all {features_name}, set {n_top_columns_parameter} to None. "
          f"Alternatively, you can set parameter {columns_parameter} to a list of the specific {features_name} "
          f"you want to run on.",
          UserWarning,
      )
  
file_path: deepchecks/deepchecks/nlp/utils/weak_segments.py
imports: import warnings
  from typing import Hashable, List, Optional, Union
  import numpy
  from deepchecks.core.errors import DeepchecksNotSupportedError, DeepchecksProcessError
  from deepchecks.nlp import TextData
  from deepchecks.utils.dataframes import select_from_dataframe
label_desc: Use image node_id 2 for calling a global function with example usage: _warn_n_top_columns(data_type, n_top_features) without return types