code: def vectors_for_testing(vector, is_feature1d=True):
      """
      Function generate different possible variations of one vector.
      That feature useful for testing algorithms input data.
  
      Parameters
      ----------
      vector : ndarray
          Vector that would be transformed in different data types.
      is_feature1d : bool
          Parameter explain the vector type. Parameter equal to ``True`` mean
          that input data a banch of samples that contains one feature each.
          Defaults to ``True``.
  
      Raises
      ------
      ValueError
          If input is not a vector
  
      Returns
      -------
      list
          List that contains the same vectors in different data types like
          numpy 2D vector or pandas Data Frame
      """
  
      if vector.ndim != 1 and min(vector.shape) != 1:
          raise ValueError("Input should be a vector")
  
      shape2d = (vector.size, 1) if is_feature1d else (1, vector.size)
  
      vectors_list = []
      if vector.ndim == 1:
          vectors_list.extend([vector, pd.Series(vector)])
  
      vectors_list.extend(
          [
              vector.reshape(shape2d),
              pd.DataFrame(vector.reshape(shape2d)),
          ]
      )
  
      return vectors_list
  
file_path: neupy/tests/helpers.py
imports: import sys
  from contextlib import contextmanager
  import six
  import numpy
  import pandas
  from sklearn import datasets
  from sklearn.model_selection import StratifiedShuffleSplit
  from matplotlib import pyplot
  from neupy import algorithms, layers, utils, init
  from neupy.storage import save_dict, load_dict
label_desc: Use image node_id 5 for calling a global function with example usage: vectors_for_testing(vector, is_feature1d) and returns: vectors_list