          -------
          tensor
              The manipulated input tensor
          """
          if get_backend() == "amd":
              batch, height, width, depth = input_tensor.shape.dims
              new_height = height // self.scale
              new_width = width // self.scale
              reshaped = K.reshape(input_tensor,
                                   (batch, new_height, self.scale, new_width, self.scale, depth))
              retval = K.reshape(K.permute_dimensions(reshaped, [0, 1, 3, 2, 4, 5]),
                                 (batch, new_height, new_width, -1))
          else:
              retval = tf.nn.space_to_depth(input_tensor, block_size=self.scale, data_format="NHWC")
          logger.debug("Input shape: %s, Output shape: %s", input_tensor.shape, retval.shape)
          return retval
  
      def get_config(self):
          """ Return the ICNR Initializer configuration.
 
</s> remove         dim = K.int_shape(image) if self._batch_shape is None else self._batch_shape
 </s> add         dim = K.int_shape(image) </s> remove         return KerasModel(input_, outputs=outputs, name="decoder_a")
 </s> add         return KModel(input_, outputs=outputs, name="decoder_a") </s> remove             retval = (os.path.basename(prefix) == "envs")
 </s> add             retval = os.path.basename(prefix) == "envs" </s> remove         retval = np.stack([cast(np.ndarray, feed_face.face)[..., :3]
 </s> add         retval = np.stack([T.cast(np.ndarray, feed_face.face)[..., :3] </s> remove             retval = dict(a=decoder(inputs["a"]), b=decoder(inputs["b"]))
 </s> add             retval = {"a": decoder(inputs["a"]), "b": decoder(inputs["b"])}
        retval = tf.nn.space_to_depth(input_tensor, block_size=self.scale, data_format="NHWC")
          -------
          tensor
              The manipulated input tensor
          """
        retval = tf.nn.space_to_depth(input_tensor, block_size=self.scale, data_format="NHWC")
        retval = tf.nn.space_to_depth(input_tensor, block_size=self.scale, data_format="NHWC")
        retval = tf.nn.space_to_depth(input_tensor, block_size=self.scale, data_format="NHWC")
        retval = tf.nn.space_to_depth(input_tensor, block_size=self.scale, data_format="NHWC")
        retval = tf.nn.space_to_depth(input_tensor, block_size=self.scale, data_format="NHWC")
        retval = tf.nn.space_to_depth(input_tensor, block_size=self.scale, data_format="NHWC")
        retval = tf.nn.space_to_depth(input_tensor, block_size=self.scale, data_format="NHWC")
        retval = tf.nn.space_to_depth(input_tensor, block_size=self.scale, data_format="NHWC")
        retval = tf.nn.space_to_depth(input_tensor, block_size=self.scale, data_format="NHWC")
        retval = tf.nn.space_to_depth(input_tensor, block_size=self.scale, data_format="NHWC")
          logger.debug("Input shape: %s, Output shape: %s", input_tensor.shape, retval.shape)
          return retval
  
      def get_config(self):
          """ Return the ICNR Initializer configuration.
keep keep keep keep replace replace replace replace replace replace replace replace replace replace keep keep keep keep keep
 Remove plaidML support (#1325)

* Remove PlaidML reference from readme files

* Remove AMD option from installers

* remove amd requirements and update setup.py

* remove plaidml test from CI workflow

* gpustats: remove plaidml backend

* plaid removals:
  - faceswap.py - python version check
  - setup.cfg - plaidml typing ignore
  - lib.keras_utils - All plaid code
  - lib.launcher.py - All plaidml checks and configuration

* remove tf2.2 specific code from GUI event reader

* lib.model - remove all plaidml implementations

* plugins.extract - remove plaidml code

* plugins.train remove plaidml code

* lib.convert - remove plaidml code

* tools.model: remove plaidml code

* Remove plaidML tests from unit tests

* remove plaidml_utils and docsting cleanups

* Remove plaidML refs from configs

* fix keras imports 
lib/model/initializers.py