code: def _build_graph(self):
      # create Graph
      self.model_input = tf.placeholder(
          dtype=tf.int32, shape=(None, self.max_len)
      )
      self.v = tf.placeholder(
          dtype=tf.float32, shape=(None, self.output_dimension)
      )
      self.sample_weight = tf.placeholder(
          dtype=tf.float32, shape=(None,)
      )
      self.embedding_weight = tf.Variable(initial_value=self.init_W)
  
      self.seq_emb = tf.nn.embedding_lookup(
          self.embedding_weight, self.model_input
      )
      self.reshape = tf.reshape(
          self.seq_emb, [-1, self.max_len, self.emb_dim, 1]
      )
      self.convs = []
  
      # Convolutional layers
      for i in self.filter_lengths:
          convolutional_layer, weights = conv_layer(
              input=self.reshape,
              num_input_channels=1,
              filter_height=i,
              filter_width=self.emb_dim,
              num_filters=self.nb_filters,
              use_pooling=True,
          )
  
          flat_layer, _ = flatten_layer(convolutional_layer)
          self.convs.append(flat_layer)
  
      self.model_output = tf.concat(self.convs, axis=-1)
      # Fully-connected layers
      self.model_output = fc_layer(
          input=self.model_output,
          num_input=self.model_output.get_shape()[-1],
          num_output=self.vanila_dimension,
      )
      # Dropout layer
      self.model_output = tf.nn.dropout(
          self.model_output, self.drop_rate
      )
      # Output layer
      self.model_output = fc_layer(
          input=self.model_output,
          num_input=self.vanila_dimension,
          num_output=self.output_dimension,
      )
      # Weighted MEA loss function
      self.mean_square_loss = tf.losses.mean_squared_error(
          labels=self.v,
          predictions=self.model_output,
          reduction=tf.losses.Reduction.NONE,
      )
      self.weighted_loss = tf.reduce_sum(
          tf.reduce_sum(self.mean_square_loss, axis=1, keepdims=True)
          * self.sample_weight
      )
      # RMSPro optimizer
      self.optimizer = tf.train.RMSPropOptimizer(
          learning_rate=self.learning_rate
      ).minimize(self.weighted_loss)
  
imports: import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the CNN_module obj's underlying member method code with example usage: obj._build_graph() without return types