docstr: Method helps test network prediction training using different
  types of row or column vector.
docstr_tok: ["Method","helps","test","network","prediction","training","using","different","types","of","row","or","column","vector","."]
code: def assertInvalidVectorTrain(
      self,
      network,
      input_vector,
      target=None,
      decimal=5,
      is_feature1d=True,
      **train_kwargs,
  ):
      """
      Method helps test network prediction training using different
      types of row or column vector.
      """
      input_vectors = vectors_for_testing(input_vector, is_feature1d)
  
      if target is not None:
          target_vectors = vectors_for_testing(target, is_feature1d)
          input_vectors = zip(input_vectors, target_vectors)
  
      train_args = inspect.getargspec(network.train).args
  
      if "epochs" in train_args and "epochs" not in train_kwargs:
          train_kwargs["epochs"] = 5
  
      elif "epsilon" in train_args and "epsilon" not in train_kwargs:
          train_kwargs["epsilon"] = 0.1
  
      for i, X in enumerate(input_vectors, start=1):
          if target is None:
              network.train(X, **train_kwargs)
          else:
              network.train(*X, **train_kwargs)
  
imports: import pickle
  import inspect
  import logging
  import unittest
  import numpy
  import tensorflow
  from neupy import utils, layers, init
  from neupy.utils import tensorflow_eval, tensorflow_session, shape_to_tuple
  from neupy.layers.base import format_name_if_specified_as_pattern
  from helpers import vectors_for_testing
label_desc: Use image node_id 5 for calling the BaseTestCase obj's underlying member method code with example usage: obj.assertInvalidVectorTrain(network, input_vector, target, decimal, is_feature1d) without return types