code: def build_model(self, n_features, n_classes):
          
          self._create_placeholders(n_features, n_classes)
          self._create_variables(n_features, n_classes)
  
          self.mod_y = tf.nn.softmax(
              tf.add(tf.matmul(self.input_data, self.W_), self.b_))
  
          self.cost = self.loss.compile(self.mod_y, self.input_labels)
          self.train_step = tf.train.GradientDescentOptimizer(
              self.learning_rate).minimize(self.cost)
          self.accuracy = Evaluation.accuracy(self.mod_y, self.input_labels)
docs: Create the computational graph.
  
          :param n_features: number of features
          :param n_classes: number of classes
          :return: self