              var_t += wd_t * handle
  
          var_update = self._resource_scatter_add(handle,
                                                  indices,
                                                  tf.gather(tf.math.negative(lr_t) * var_t, indices))
  
          updates = [var_update, m_t, v_t]
          if self.amsgrad:
              updates.append(vhat_t)
          return tf.group(*updates)
 
</s> remove         m_t_indices = tf.gather(m_t, indices)
 </s> add         m_t_indices = tf.gather(m_t, indices)  # pylint:disable=no-value-for-parameter </s> remove             partial_x, _ = tf.split(inputs,
                                    [partial_size, layer_size - partial_size],
                                    axis=self.axis)
 </s> add             partial_x, _ = tf.split(  # pylint:disable=redundant-keyword-arg,no-value-for-parameter
                inputs,
                [partial_size, layer_size - partial_size],
                axis=self.axis) </s> remove         # pylint:disable=too-many-locals
 </s> add         # pylint:disable=too-many-locals, unused-argument </s> remove         self.kernel_size = kernel_size
 </s> add         self.filter_size = filter_size
        self.filter_sigma = filter_sigma </s> remove 

@pytest.mark.parametrize('dummy', [None], ids=[get_backend().upper()])
def test_dssim_channels_last(dummy):  # pylint:disable=unused-argument
    """ Basic test for DSSIM Loss """
    prev_data = K.image_data_format()
    K.set_image_data_format('channels_last')
    for input_dim, kernel_size in zip([32, 33], [2, 3]):
        input_shape = [input_dim, input_dim, 3]
        var_x = np.random.random_sample(4 * input_dim * input_dim * 3)
        var_x = var_x.reshape([4] + input_shape)
        var_y = np.random.random_sample(4 * input_dim * input_dim * 3)
        var_y = var_y.reshape([4] + input_shape)

        model = Sequential()
        model.add(Conv2D(32, (3, 3), padding='same', input_shape=input_shape,
                         activation='relu'))
        model.add(Conv2D(3, (3, 3), padding='same', input_shape=input_shape,
                         activation='relu'))
        adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
        model.compile(loss=losses.DSSIMObjective(kernel_size=kernel_size),
                      metrics=['mse'],
                      optimizer=adam)
        model.fit(var_x, var_y, batch_size=2, epochs=1, shuffle='batch')

        # Test same
        x_1 = K.constant(var_x, 'float32')
        x_2 = K.constant(var_x, 'float32')
        dssim = losses.DSSIMObjective(kernel_size=kernel_size)
        assert_allclose(0.0, K.eval(dssim(x_1, x_2)), atol=1e-4)

        # Test opposite
        x_1 = K.zeros([4] + input_shape)
        x_2 = K.ones([4] + input_shape)
        dssim = losses.DSSIMObjective(kernel_size=kernel_size)
        assert_allclose(0.5, K.eval(dssim(x_1, x_2)), atol=1e-4)

    K.set_image_data_format(prev_data)
 </s> add 
                                                tf.gather(  # pylint:disable=no-value-for-parameter
                                                    tf.math.negative(lr_t) * var_t,
                                                    indices))
              var_t += wd_t * handle
  
          var_update = self._resource_scatter_add(handle,
                                                  indices,
                                                tf.gather(  # pylint:disable=no-value-for-parameter
                                                    tf.math.negative(lr_t) * var_t,
                                                    indices))
  
          updates = [var_update, m_t, v_t]
          if self.amsgrad:
              updates.append(vhat_t)
          return tf.group(*updates)
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