              wd_t = tf.cast(wd_t(self.iterations), var_dtype)
          return wd_t
  
      def _resource_apply_dense(self, grad, handle, apply_state=None):
          # pylint:disable=too-many-locals
          """ Add ops to apply dense gradients to the variable handle.
  
          Parameters
          ----------
          grad: Tensor
 
</s> remove         # pylint:disable=too-many-locals
 </s> add         # pylint:disable=too-many-locals, unused-argument </s> remove     def call(self, inputs, **kwargs):
 </s> add     def call(self, inputs, *args, **kwargs): </s> remove         self.c_1 = (self.k_1 * self.max_value) ** 2
        self.c_2 = (self.k_2 * self.max_value) ** 2
        self.dim_ordering = K.image_data_format()

    @staticmethod
    def _int_shape(input_tensor):
        """ Returns the shape of tensor or variable as a tuple of int or None entries.

        Parameters
        ----------
        input_tensor: tensor or variable
            The input to return the shape for

        Returns
        -------
        tuple
            A tuple of integers (or None entries)
        """
        return K.int_shape(input_tensor)
 </s> add  </s> remove                                                 tf.gather(tf.math.negative(lr_t) * var_t, indices))
 </s> add                                                 tf.gather(  # pylint:disable=no-value-for-parameter
                                                    tf.math.negative(lr_t) * var_t,
                                                    indices)) </s> remove             Additional keyword arguments
 </s> add             Additional standard keras Layer keyword arguments
        # pylint:disable=too-many-locals,unused-argument
              wd_t = tf.cast(wd_t(self.iterations), var_dtype)
          return wd_t
  
      def _resource_apply_dense(self, grad, handle, apply_state=None):
        # pylint:disable=too-many-locals,unused-argument
          """ Add ops to apply dense gradients to the variable handle.
  
          Parameters
          ----------
          grad: Tensor
keep keep keep keep replace keep keep keep keep keep
 Update code to support Tensorflow versions up to 2.8 (#1213)

* Update maximum tf version in setup + requirements

* - bump max version of tf version in launcher
- standardise tf version check

* update keras get_custom_objects  for tf>2.6

* bugfix: force black text in GUI file dialogs (linux)

* dssim loss - Move to stock tf.ssim function

* Update optimizer imports for compatibility

* fix logging for tf2.8

* Fix GUI graphing for TF2.8

* update tests

* bump requirements.txt versions

* Remove limit on nvidia-ml-py

* Graphing bugfixes
  - Prevent live graph from displaying if data not yet available

* bugfix: Live graph. Collect loss labels correctly

* fix: live graph - swallow inconsistent loss errors

* Bugfix: Prevent live graph from clearing during training

* Fix graphing for AMD 
lib/model/optimizers_tf.py