Tile latent and concatenate to image across depth.

  Args:
    image: 4-D Tensor, (batch_size X height X width X channels)
    latent: 2-D Tensor, (batch_size X latent_dims)
    concat_latent: If set to False, the image is returned as is.

  Returns:
    concat_latent: 4-D Tensor, (batch_size X height X width X channels+1)
      latent tiled and concatenated to the image across the channels.

def tile_and_concat(image, latent, concat_latent=True):
  """Tile latent and concatenate to image across depth.

  Args:
    image: 4-D Tensor, (batch_size X height X width X channels)
    latent: 2-D Tensor, (batch_size X latent_dims)
    concat_latent: If set to False, the image is returned as is.

  Returns:
    concat_latent: 4-D Tensor, (batch_size X height X width X channels+1)
      latent tiled and concatenated to the image across the channels.
  """
  if not concat_latent:
    return image
  image_shape = common_layers.shape_list(image)
  latent_shape = common_layers.shape_list(latent)
  height, width = image_shape[1], image_shape[2]
  latent_dims = latent_shape[1]
  height_multiples = height // latent_dims
  pad = height - (height_multiples * latent_dims)
  latent = tf.reshape(latent, (-1, latent_dims, 1, 1))
  latent = tf.tile(latent, (1, height_multiples, width, 1))
  latent = tf.pad(latent, [[0, 0], [pad // 2, pad // 2], [0, 0], [0, 0]])
  return tf.concat([image, latent], axis=-1)