class_docstr: Variant C of the three InceptionResNet layers described in
  https://arxiv.org/abs/1710.02238. All variants use multiple
  convolutional blocks with varying kernel sizes and number of filters. This
  allows capturing patterns over different scales in the inputs. Residual
  connections are additionally used and have been shown previously to improve
  convergence and training in deep networks. A 1x1 convolution is used on the
  concatenated feature maps from the different convolutional blocks, to ensure
  shapes of inputs and feature maps are same for the residual connection.
docstr: Builds the layers components and set _layers attribute.
docstr_tok: ["Builds","the","layers","components","and","set","_layers","attribute","."]
code: def _build_layer_components(self):
      """Builds the layers components and set _layers attribute."""
      self.conv_block1 = [
          Conv2D(
              self.num_filters,
              kernel_size=(1, 1),
              strides=1,
              padding="same",
              activation=tf.nn.relu,
          )
      ]
  
      self.conv_block2 = [
          Conv2D(
              filters=self.num_filters,
              kernel_size=1,
              strides=1,
              activation=tf.nn.relu,
              padding="same",
          )
      ]
      self.conv_block2.append(
          Conv2D(
              filters=int(self.num_filters * 1.16),
              kernel_size=(1, 3),
              strides=1,
              activation=tf.nn.relu,
              padding="same",
          )
      )
      self.conv_block2.append(
          Conv2D(
              filters=int(self.num_filters * 1.33),
              kernel_size=(3, 1),
              strides=1,
              activation=tf.nn.relu,
              padding="same",
          )
      )
  
      self.conv_block3 = [
          Conv2D(
              filters=self.input_dim,
              kernel_size=(1, 1),
              strides=1,
              padding="same",
          )
      ]
  
      self.concat_layer = Concatenate()
      self.add_layer = Add()
      self.activation_layer = ReLU()
  
      self._layers = (
          self.conv_block1 + self.conv_block2 + self.conv_block3
      )
      self._layers.extend(
          [self.concat_layer, self.add_layer, self.activation_layer]
      )
  
imports: import tensorflow
  from tensorflow.keras.layers import Conv2D, Concatenate, ReLU, Add, MaxPool2D
label_desc: Use image node_id 2 for calling the InceptionResnetC obj's underlying member method code with example usage: obj._build_layer_components() without return types