          -------
          Tensor
              The image tensor in XYZ format
          """
          dim = K.int_shape(image) if self._batch_shape is None else self._batch_shape
          image = K.permute_dimensions(image, (0, 3, 1, 2))
          image = K.reshape(image, (dim[0], dim[3], dim[1] * dim[2]))
          converted = K.permute_dimensions(K.dot(mapping, image), (1, 2, 0))
          return K.reshape(converted, dim)
  
 
</s> remove         self._model: Optional[keras.models.Model] = None
 </s> add         self._model: T.Optional[tf.keras.models.Model] = None </s> remove         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")
 </s> add         retval = tf.nn.space_to_depth(input_tensor, block_size=self.scale, data_format="NHWC") </s> remove         figures: Dict[Literal["a", "b"], np.ndarray] = {}
        headers: Dict[Literal["a", "b"], np.ndarray] = {}
 </s> add         figures: T.Dict[Literal["a", "b"], np.ndarray] = {}
        headers: T.Dict[Literal["a", "b"], np.ndarray] = {} </s> remove         feeds: Dict[Literal["a", "b"], np.ndarray] = {}
 </s> add         feeds: T.Dict[Literal["a", "b"], np.ndarray] = {}
        dim = K.int_shape(image)
          -------
          Tensor
              The image tensor in XYZ format
          """
        dim = K.int_shape(image)
          image = K.permute_dimensions(image, (0, 3, 1, 2))
          image = K.reshape(image, (dim[0], dim[3], dim[1] * dim[2]))
          converted = K.permute_dimensions(K.dot(mapping, image), (1, 2, 0))
          return K.reshape(converted, dim)
  
keep keep keep keep 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/keras_utils.py