original_docstring: 
          creates input nodes for an autoencoder from the dataset
  
          Sets:
              x, x_target
          
docstring: creates input nodes for an autoencoder from the dataset
  Sets:
  x, x_target
docstring_tokens: creates input nodes for an autoencoder from the dataset Sets : x x_target
code: def create_input_nodes(self, dataset):
          """
          creates input nodes for an autoencoder from the dataset
  
          Sets:
              x, x_target
          """
  
          super(HM, self).create_input_nodes(dataset=dataset)
          self.input_shape = self.x.shape.as_list()[1:]
          self.b_size = tf.shape(self.x)[0]
          self.n_z_samples = NrSamples.process_n_samples(self.samples, self.global_epoch)
          self.b_size_int = self.batch_size['train']
          self.n_z_samples_int = self.samples
code_tokens: def create_input_nodes ( self , dataset ) : super ( HM , self ) . create_input_nodes ( dataset = dataset ) self . input_shape = self . x . shape . as_list ( ) [ 1 : ] self . b_size = tf . shape ( self . x ) [ 0 ] self . n_z_samples = NrSamples . process_n_samples ( self . samples , self . global_epoch ) self . b_size_int = self . batch_size [ 'train' ] self . n_z_samples_int = self . samples
short_docstring: creates input nodes for an autoencoder from the dataset
  Sets:
  x, x_target
short_docstring_tokens: creates input nodes for an autoencoder from the dataset Sets : x x_target
comment: """
          creates input nodes for an autoencoder from the dataset
  
          Sets:
              x, x_target
          """