class_docstr: A class for modifying a neural nework using many of the operations above. 
class_docstr_tok: ["A","class","for","modifying","a","neural","nework","using","many","of","the","operations","above","."]
docstr: Returns true if it is a valid network. 
docstr_tok: ["Returns","true","if","it","is","a","valid","network","."]
code: def _is_a_valid_network(self, nn):
      """Returns true if it is a valid network."""
      if nn is None:
          return False
      elif self.constraint_checker is None:
          return True
      else:
          return self.constraint_checker(nn)
  
imports: from argparse import Namespace
  from copy import deepcopy
  import numpy
  from .neural_network import ConvNeuralNetwork, MultiLayerPerceptron, MLP_RECTIFIERS, MLP_SIGMOIDS, is_a_pooling_layer_label, is_a_conv_layer_label, CNNImageSizeMismatchException, CNNNoConvAfterIPException
  from ..utils.general_utils import reorder_list_or_array, reorder_rows_and_cols_in_matrix
  from ..utils.option_handler import get_option_specs, load_options
  from ..utils.reporters import get_reporter
label_desc: Use image node_id 5 for calling the NNModifier obj's underlying member method code with example usage: obj._is_a_valid_network(nn) and returns: False, True, self