code: def _shape_check(X, min_value_cols):
      if min_value_cols > 1:
          if X.ndim == 1 or X.shape[1] < 2:
              raise ValueError(
                  f"0 feature(s) (shape={X.shape}) while a minimum of {min_value_cols} is required."
              )
      else:
          if X.ndim == 2 and X.shape[1] < 1:
              raise ValueError(
                  f"0 feature(s) (shape={X.shape}) while a minimum of {min_value_cols} is required."
              )
  
file_path: scikit-lego/sklego/meta/_grouped_utils.py
imports: from typing import Tuple
  import numpy
  import pandas
  from scipy.sparse import issparse
  from sklearn.utils import check_array
  from sklearn.utils.validation import _ensure_no_complex_data
  from sklego.common import as_list
label_desc: Use image node_id 6 for calling a global function with example usage: _shape_check(X, min_value_cols) without return types