code: def _image_channel_compress_bottom(inputs, model_hparams, name="bottom"):
    
    num_channels = 3
    with tf.variable_scope(name):
      inputs = tf.to_float(inputs)
      hp = model_hparams
      if hp.mode != tf.estimator.ModeKeys.PREDICT:
        tf.summary.image(
            "inputs",
            common_layers.tpu_safe_image_summary(inputs),
            max_outputs=2)
      inputs = common_layers.convert_rgb_to_symmetric_real(inputs)
  
      # Reshape inputs to apply convolutions across [img_len, img_len*channels].
      inputs_shape = common_layers.shape_list(inputs)
      inputs = tf.reshape(
          inputs, [-1, inputs_shape[1], inputs_shape[2] * inputs_shape[3], 1])
  
      # Compress RGB intensities for each pixel using a convolution.
      outputs = tf.layers.conv2d(
          inputs,
          model_hparams.hidden_size,
          kernel_size=(1, num_channels),
          padding="VALID",
          strides=(1, num_channels),
          activation=tf.nn.relu,
          name="conv_input")
      return outputs
docs: Compresses channel-wise input pixels into whole pixel representions.
  
    Perform conversion of RGB pixel values to a real number in the range -1 to
    1. This combines pixel channels to form a representation of shape
    [img_len, img_len].
  
    Args:
      inputs: Tensor representing RGB pixel intensities as integers, of shape
        [batch, img_len, img_len, channels].
      model_hparams: HParams, model hyperparmeters.
      name: string, scope.
  
    Returns:
      body_input: Tensor of shape
        [batch, img_len, img_len, model_hparams.hidden_size].