code: def build_model(self, n_features, n_classes):
          
          self._create_placeholders(n_features, n_classes)
          self._create_variables(n_features)
  
          next_train = self._create_encoding_layers()
          self.mod_y, _, _ = Layers.linear(next_train, n_classes)
          self.layer_nodes.append(self.mod_y)
  
          self.cost = self.loss.compile(self.mod_y, self.input_labels)
          self.train_step = self.trainer.compile(self.cost)
          self.accuracy = Evaluation.accuracy(self.mod_y, self.input_labels)
docs: Create the computational graph.
  
          This graph is intented to be created for finetuning,
          i.e. after unsupervised pretraining.
          :param n_features: Number of features.
          :param n_classes: number of classes.
          :return: self