              section=section, title="dssim_loss", datatype=bool, default=False,
              info="Use DSSIM for Loss rather than Mean Absolute Error\n"
                   "May increase overall quality.")
          self.add_item(
              section=section, title="mask_type", datatype=str, default="none",
              choices=MASK_TYPES, info=MASK_INFO)
          self.add_item(
              section=section, title="nodes", datatype=int, default=1024, rounding=64,
 
</s> remove         # TODO Look to re-instate clipnorm
        # Clipnorm is ballooning VRAM useage, which is not expected behaviour
        # and may be a bug in Keras/TF?
        # For now this is commented out, but revisit in future to reinstate

        # # PlaidML has a bug regarding the clipnorm parameter
        # # See: https://github.com/plaidml/plaidml/issues/228
        # # Workaround by simply removing it.
        # # TODO: Remove this as soon it is fixed in PlaidML.
        # if keras.backend.backend() == "plaidml.keras.backend":
        #    optimizer = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999)
        # else:
        #    optimizer = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, clipnorm=1.0)
        optimizer = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999)
 </s> add         optimizer = self.get_optimizer(lr=5e-5, beta_1=0.5, beta_2=0.999)
        self.add_item(
            section=section, title="clipnorm", datatype=bool, default=True,
            info="Controls gradient clipping of the optimizer. Can prevent model corruption at "
                 "the expense of VRAM")
              section=section, title="dssim_loss", datatype=bool, default=False,
              info="Use DSSIM for Loss rather than Mean Absolute Error\n"
                   "May increase overall quality.")
        self.add_item(
            section=section, title="clipnorm", datatype=bool, default=True,
            info="Controls gradient clipping of the optimizer. Can prevent model corruption at "
                 "the expense of VRAM")
          self.add_item(
              section=section, title="mask_type", datatype=str, default="none",
              choices=MASK_TYPES, info=MASK_INFO)
          self.add_item(
              section=section, title="nodes", datatype=int, default=1024, rounding=64,
keep keep add keep keep keep keep keep
 Add clipnorm back in as optimizer option for unbalanced 
plugins/train/_config.py