def build_subnetwork(self,
                         features,
                         logits_dimension,
                         training,
                         iteration_step,
                         summary,
                         previous_ensemble=None):
        input_layer = tf.to_float(features[FEATURES_KEY])
        kernel_initializer = tf.glorot_uniform_initializer(seed=self._seed)
        last_layer = input_layer
        for _ in range(self._num_layers):
            last_layer = tf.layers.dense(
                last_layer,
                units=self._layer_size,
                activation=tf.nn.relu,
                kernel_initializer=kernel_initializer)
        logits = tf.layers.dense(
            last_layer,
            units=logits_dimension,
            kernel_initializer=kernel_initializer)
        persisted_tensors = {_NUM_LAYERS_KEY: tf.constant(self._num_layers)}
        return adanet.Subnetwork(
            last_layer=last_layer,
            logits=logits,
            complexity=self._measure_complexity(),
            persisted_tensors=persisted_tensors)

def build_subnetwork(self,
                         features,
                         logits_dimension,
                         training,
                         iteration_step,
                         summary,
                         previous_ensemble=None):
        input_layer = tf.to_float(features[FEATURES_KEY])
        kernel_initializer = tf.glorot_uniform_initializer(seed=self._seed)
        last_layer = input_layer
        for _ in range(self._num_layers):
            last_layer = tf.keras.layers.Dense(
                units=64, activation="relu", kernel_initializer=kernel_initializer,
                use_bias=True, bias_initializer=kernel_initializer)(last_layer)
        logits = tf.keras.layers.Dense(
            units=2, activation=None, kernel_initializer=kernel_initializer,
            use_bias=True, bias_initializer=kernel_initializer)(last_layer)
        persisted_tensors = {_NUM_LAYERS_KEY: tf.constant(self._num_layers)}
        return adanet.Subnetwork(
            last_layer=last_layer,
            logits=logits,
            complexity=self._measure_complexity(),
            persisted_tensors=persisted_tensors)