Minor fixes

      return

    self._ensure_built()

    self._inputs_built = True

    if len(self.model.inputs) > 0:

    if (self.model.inputs is not None) and len(self.model.inputs) > 0:

      self._input_shapes = [t.shape for t in self.model.inputs]

      self._input_dtypes = [t.dtype.as_numpy_dtype for t in self.model.inputs]

    else:

      loss = self._loss_fn

    var_key = None

    if variables is not None:

      var_key = tuple(v.experimental_ref() for v in variables)

      var_key = tuple(v.ref() for v in variables)

      # The optimizer creates internal variables the first time apply_gradients()

      # is called for a new set of variables.  If that happens inside a function

        inputs = inputs[0]

      if outputs is not None:

        outputs = tuple(outputs)

        key = tuple(t.experimental_ref() for t in outputs)

        key = tuple(t.ref() for t in outputs)

        if key not in self._output_functions:

          self._output_functions[key] = tf.keras.backend.function(

              self.model.inputs, outputs)

      dest_vars = dest_vars[:-2]

    for source_var, dest_var in zip(source_vars, dest_vars):

      assignment_map[source_var.experimental_ref()] = dest_var

      assignment_map[source_var.ref()] = dest_var

    return assignment_map

    source_vars = source_model.model.trainable_variables

    for source_var in source_vars:

      value_map[source_var.experimental_ref()] = source_var.numpy()

      value_map[source_var.ref()] = source_var.numpy()

    return value_map