How to use the csbdeep.utils.six.FileNotFoundError function in csbdeep

def _build():
        with pytest.raises(FileNotFoundError):
            CARE(None,basedir=str(tmpdir))

        CARE(config,name='model',basedir=None)
        with pytest.raises(ValueError):
            CARE(None,basedir=None)

        CARE(config,basedir=str(tmpdir)).export_TF()

        with pytest.warns(UserWarning):
            CARE(config,name='model',basedir=str(tmpdir))
            CARE(config,name='model',basedir=str(tmpdir))
            CARE(None,name='model',basedir=str(tmpdir))
    if config.is_valid():

def _set_logdir(self):
        self.logdir = self.basedir / self.name

        config_file =  self.logdir / 'config.json'
        if self.config is None:
            if config_file.exists():
                config_dict = load_json(str(config_file))
                self.config = self._config_class(**config_dict)
                if not self.config.is_valid():
                    invalid_attr = self.config.is_valid(True)[1]
                    raise ValueError('Invalid attributes in loaded config: ' + ', '.join(invalid_attr))
            else:
                raise FileNotFoundError("config file doesn't exist: %s" % str(config_file.resolve()))
        else:
            if self.logdir.exists():
                warnings.warn('output path for model already exists, files may be overwritten: %s' % str(self.logdir.resolve()))
            self.logdir.mkdir(parents=True, exist_ok=True)
            save_json(vars(self.config), str(config_file))

├── source1
        │   ├── imageA.tif
        │   └── imageB.tif
        └── source2
            ├── imageA.tif
            └── imageC.tif

        >>> data = RawData.from_folder(basepath='data', source_dirs=['source1','source2'], target_dir='GT', axes='YX')
        >>> n_images = data.size
        >>> for source_x, target_y, axes, mask in data.generator():
        ...     pass

        """
        p = Path(basepath)
        pairs = [(f, p/target_dir/f.name) for f in chain(*((p/source_dir).glob(pattern) for source_dir in source_dirs))]
        len(pairs) > 0 or _raise(FileNotFoundError("Didn't find any images."))
        consume(t.exists() or _raise(FileNotFoundError(t)) for s,t in pairs)
        axes = axes_check_and_normalize(axes)
        n_images = len(pairs)
        description = "{p}: target='{o}', sources={s}, axes='{a}', pattern='{pt}'".format(p=basepath, s=list(source_dirs),
                                                                                          o=target_dir, a=axes, pt=pattern)

        def _gen():
            for fx, fy in pairs:
                x, y = imread(str(fx)), imread(str(fy))
                len(axes) >= x.ndim or _raise(ValueError())
                yield x, y, axes[-x.ndim:], None

        return RawData(_gen, n_images, description)

│   ├── imageA.tif
        │   └── imageB.tif
        └── source2
            ├── imageA.tif
            └── imageC.tif

        >>> data = RawData.from_folder(basepath='data', source_dirs=['source1','source2'], target_dir='GT', axes='YX')
        >>> n_images = data.size
        >>> for source_x, target_y, axes, mask in data.generator():
        ...     pass

        """
        p = Path(basepath)
        pairs = [(f, p/target_dir/f.name) for f in chain(*((p/source_dir).glob(pattern) for source_dir in source_dirs))]
        len(pairs) > 0 or _raise(FileNotFoundError("Didn't find any images."))
        consume(t.exists() or _raise(FileNotFoundError(t)) for s,t in pairs)
        axes = axes_check_and_normalize(axes)
        n_images = len(pairs)
        description = "{p}: target='{o}', sources={s}, axes='{a}', pattern='{pt}'".format(p=basepath, s=list(source_dirs),
                                                                                          o=target_dir, a=axes, pt=pattern)

        def _gen():
            for fx, fy in pairs:
                x, y = imread(str(fx)), imread(str(fy))
                len(axes) >= x.ndim or _raise(ValueError())
                yield x, y, axes[-x.ndim:], None

        return RawData(_gen, n_images, description)

def _set_logdir(self):
        self.logdir = self.basedir / self.name

        config_file =  self.logdir / 'config.json'
        if self.config is None:
            if config_file.exists():
                config_dict = load_json(str(config_file))
                self.config = self._config_class(np.array([]), **config_dict)
                if not self.config.is_valid():
                    invalid_attr = self.config.is_valid(True)[1]
                    raise ValueError('Invalid attributes in loaded config: ' + ', '.join(invalid_attr))
            else:
                raise FileNotFoundError("config file doesn't exist: %s" % str(config_file.resolve()))
        else:
            if self.logdir.exists():
                warnings.warn('output path for model already exists, files may be overwritten: %s' % str(self.logdir.resolve()))
            self.logdir.mkdir(parents=True, exist_ok=True)
            save_json(vars(self.config), str(config_file))

value_manipulation=manipulator)

        history = self.keras_model.fit_generator(generator=training_data, validation_data=(validation_X, validation_Y),
                                                 epochs=epochs, steps_per_epoch=steps_per_epoch,
                                                 callbacks=self.callbacks, verbose=1)

        if self.basedir is not None:
            self.keras_model.save_weights(str(self.logdir / 'weights_last.h5'))

            if self.config.train_checkpoint is not None:
                print()
                self._find_and_load_weights(self.config.train_checkpoint)
                try:
                    # remove temporary weights
                    (self.logdir / 'weights_now.h5').unlink()
                except FileNotFoundError:
                    pass

        return history