          deactivate_folder = os.path.join(conda_prefix, "etc", "conda", "deactivate.d")
          os.makedirs(activate_folder, exist_ok=True)
          os.makedirs(deactivate_folder, exist_ok=True)
  
          ext = ".bat" if windows_update else ".sh"
          activate_script = os.path.join(conda_prefix, activate_folder, f"env_vars{ext}")
          deactivate_script = os.path.join(conda_prefix, deactivate_folder, f"env_vars{ext}")
  
          if os.path.isfile(activate_script):
              # Only create file if it does not already exist. There may be instances where people
              # have created their own scripts, but these should be few and far between and those
              # people should already know what they are doing.
 
</s> remove         if linux_update:
            conda_libs = os.path.join(conda_prefix, "lib")
            activate = ["#!/bin/sh\n\n",
                        "export OLD_LD_LIBRARY_PATH=${LD_LIBRARY_PATH}\n",
                        f"export LD_LIBRARY_PATH='{conda_libs}':${{LD_LIBRARY_PATH}}\n"]
            deactivate = ["#!/bin/sh\n\n",
                          "export LD_LIBRARY_PATH=${OLD_LD_LIBRARY_PATH}\n",
                          "unset OLD_LD_LIBRARY_PATH\n"]
            logger.info("Cuda search path set to '%s'", conda_libs)

        if windows_update:
            activate = ["@ECHO OFF\n",
                        "set CONDA_DLL_SEARCH_MODIFICATION_ENABLE=1\n"]
            deactivate = ["@ECHO OFF\n",
                          "set CONDA_DLL_SEARCH_MODIFICATION_ENABLE=\n"]
            logger.verbose("CONDA_DLL_SEARCH_MODIFICATION_ENABLE set to 1")  # type: ignore
 </s> add         conda_libs = os.path.join(conda_prefix, "lib")
        activate = ["#!/bin/sh\n\n",
                    "export OLD_LD_LIBRARY_PATH=${LD_LIBRARY_PATH}\n",
                    f"export LD_LIBRARY_PATH='{conda_libs}':${{LD_LIBRARY_PATH}}\n"]
        deactivate = ["#!/bin/sh\n\n",
                      "export LD_LIBRARY_PATH=${OLD_LD_LIBRARY_PATH}\n",
                      "unset OLD_LD_LIBRARY_PATH\n"]
        logger.info("Cuda search path set to '%s'", conda_libs) </s> remove         if not linux_update and not windows_update:
 </s> add         if not linux_update: </s> remove         _HELPTEXT: A string describing what this plugin does
        _DEFAULTS: A dictionary containing the options, defaults and meta information. The
                   dictionary should be defined as:
                       {<option_name>: {<metadata>}}
 </s> add         "_HELPTEXT: A string describing what this plugin does
        "_DEFAULTS: A dictionary containing the options, defaults and meta information. The
               "   dictionary should be defined as:
               "       {<option_name>: {<metadata>}} </s> remove 
        Notes
        -----
        Clip-norm is ballooning VRAM usage, which is not expected behavior and may be a bug in
        Keras/Tensorflow.

        PlaidML has a bug regarding the clip-norm parameter See:
        https://github.com/plaidml/plaidml/issues/228. We workaround by simply not adding this
        parameter for AMD backend users.
 </s> add  </s> remove _CONFIG: Dict[str, "ConfigValueType"] = {}


def KerasModel(inputs: list, outputs: list, name: str) -> keras.models.Model:  # noqa, pylint:disable=invalid-name
    """ wrapper for :class:`keras.models.Model`.

    There are some minor foibles between Keras 2.2 and the Tensorflow version of Keras, so this
    catches potential issues and fixes prior to returning the requested model.

    All models created within plugins should use this method, and should not call keras directly
    for a model.

    Parameters
    ----------
    inputs: a keras.Input object or list of keras.Input objects.
        The input(s) of the model
    outputs: keras objects
        The output(s) of the model.
    name: str
        The name of the model.

    Returns
    -------
    :class:`keras.models.Model`
        A Keras Model
    """
    if get_backend() == "amd":
        logger.debug("Flattening inputs (%s) and outputs (%s) for AMD", inputs, outputs)
        inputs = np.array(inputs).flatten().tolist()
        outputs = np.array(outputs).flatten().tolist()
        logger.debug("Flattened inputs (%s) and outputs (%s)", inputs, outputs)
    return KModel(inputs, outputs, name=name)
 </s> add _CONFIG: T.Dict[str, ConfigValueType] = {}
        activate_script = os.path.join(conda_prefix, activate_folder, "env_vars.sh")
        deactivate_script = os.path.join(conda_prefix, deactivate_folder, "env_vars.sh")
          deactivate_folder = os.path.join(conda_prefix, "etc", "conda", "deactivate.d")
          os.makedirs(activate_folder, exist_ok=True)
          os.makedirs(deactivate_folder, exist_ok=True)
  
        activate_script = os.path.join(conda_prefix, activate_folder, "env_vars.sh")
        deactivate_script = os.path.join(conda_prefix, deactivate_folder, "env_vars.sh")
        activate_script = os.path.join(conda_prefix, activate_folder, "env_vars.sh")
        deactivate_script = os.path.join(conda_prefix, deactivate_folder, "env_vars.sh")
        activate_script = os.path.join(conda_prefix, activate_folder, "env_vars.sh")
        deactivate_script = os.path.join(conda_prefix, deactivate_folder, "env_vars.sh")
  
          if os.path.isfile(activate_script):
              # Only create file if it does not already exist. There may be instances where people
              # have created their own scripts, but these should be few and far between and those
              # people should already know what they are doing.
keep keep keep keep 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 
setup.py