docstr: Function f
  
  Parameter
  ---------
  a : int
      Parameter a
  b : float
      Parameter b
  
  Results
  -------
  c : list
      Parameter c
docstr_tok: ["Function","f","Parameter","--","--","--","--","-","a",":","int","Parameter","a","b",":","float","Parameter","b","Results","--","--","--","-","c",":","list","Parameter","c"]
code: def f_bad_sections(self, X, y):
      """Function f
  
      Parameter
      ---------
      a : int
          Parameter a
      b : float
          Parameter b
  
      Results
      -------
      c : list
          Parameter c
      """
      pass
  
imports: import atexit
  import os
  import unittest
  import warnings
  import numpy
  import pytest
  from scipy import sparse
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils import _IS_WASM
  from sklearn.utils._testing import TempMemmap, _convert_container, _delete_folder, assert_allclose, assert_allclose_dense_sparse, assert_no_warnings, assert_raise_message, assert_raises, assert_raises_regex, assert_run_python_script_without_output, check_docstring_parameters, create_memmap_backed_data, ignore_warnings, raises, set_random_state
  from sklearn.utils.deprecation import deprecated
  from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS, parse_version, sp_version
  from sklearn.utils.metaestimators import available_if
label_desc: Use image node_id 2 for calling the Klass obj's underlying member method code with example usage: obj.f_bad_sections(X, y) without return types