Identical to layers.dense.

def dense(x, units, **kwargs):
  """Identical to layers.dense."""
  layer_collection = kwargs.pop("layer_collection", None)
  activations = layers().Dense(units, **kwargs)(x)
  if layer_collection:
    # We need to find the layer parameters using scope name for the layer, so
    # check that the layer is named. Otherwise parameters for different layers
    # may get mixed up.
    layer_name = tf.get_variable_scope().name
    if (not layer_name) or ("name" not in kwargs):
      raise ValueError(
          "Variable scope and layer name cannot be empty. Actual: "
          "variable_scope={}, layer name={}".format(
              layer_name, kwargs.get("name", None)))

    layer_name += "/" + kwargs["name"]
    layer_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
                                     scope=layer_name)
    assert layer_params
    if len(layer_params) == 1:
      layer_params = layer_params[0]

    tf.logging.info(
        "Registering dense layer to collection for tensor: {}".format(
            layer_params))

    x_shape = x.shape.as_list()
    if len(x_shape) == 3:
      # Handle [batch, time, depth] inputs by folding batch and time into
      # one dimension: reshaping inputs to [batchxtime, depth].
      x_2d = tf.reshape(x, [-1, x_shape[2]])
      activations_shape = activations.shape.as_list()
      activations_2d = tf.reshape(activations, [-1, activations_shape[2]])
      layer_collection.register_fully_connected_multi(
          layer_params, x_2d, activations_2d, num_uses=x_shape[1])
      activations = tf.reshape(activations_2d, activations_shape)
    else:
      layer_collection.register_fully_connected(layer_params, x, activations)
  return activations