class_docstr: DEPRECATED.
class_docstr_tok: ["DEPRECATED","."]
code: def call(self, inputs, training=True):
      inputs = tf.convert_to_tensor(inputs, dtype=self.compute_dtype)
  
      def random_height_inputs(inputs):
          """Inputs height-adjusted with random ops."""
          inputs_shape = tf.shape(inputs)
          img_hd = tf.cast(inputs_shape[-3], tf.float32)
          img_wd = inputs_shape[-2]
          height_factor = backend.random.uniform(
              shape=[],
              minval=(1.0 + self.height_lower),
              maxval=(1.0 + self.height_upper),
              seed=self.seed_generator,
          )
          adjusted_height = tf.cast(height_factor * img_hd, tf.int32)
          adjusted_size = tf.stack([adjusted_height, img_wd])
          output = tf.image.resize(
              images=inputs,
              size=adjusted_size,
              method=self.interpolation,
          )
          # tf.resize will output float32 regardless of input type.
          output = tf.cast(output, self.compute_dtype)
          output_shape = inputs.shape.as_list()
          output_shape[-3] = None
          output.set_shape(output_shape)
          return output
  
      if training:
          return random_height_inputs(inputs)
      else:
          return inputs
  
imports: from keras import backend
  from keras.api_export import keras_export
  from keras.layers.layer import Layer
  from keras.utils.module_utils import tensorflow
label_desc: Use image node_id 2 for calling the RandomHeight obj's underlying member method code with example usage: obj.call(inputs, training) and returns: output, random_height_inputs, inputs