                   normalization=None,
                   activation="leakyrelu",
                   use_depthwise=False,
                   **kwargs):
          self._name = kwargs.pop("name") if "name" in kwargs else _get_name(
              "conv_{}".format(filters))
  
          logger.debug("name: %s, filters: %s, kernel_size: %s, strides: %s, padding: %s, "
                       "normalization: %s, activation: %s, use_depthwise: %s, kwargs: %s)",
                       self._name, filters, kernel_size, strides, padding, normalization,
                       activation, use_depthwise, kwargs)
 
</s> remove         self._name = _get_name("upscale_{}".format(filters))
 </s> add         self._name = _get_name(f"upscale_{filters}") </s> remove         self._name = _get_name("residual_{}".format(filters))
 </s> add         self._name = _get_name(f"residual_{filters}") </s> remove         self._name = _get_name("separableconv2d_{}".format(filters))
 </s> add         self._name = _get_name(f"separableconv2d_{filters}") </s> remove             "conv_output_{}".format(filters))
 </s> add             f"conv_output_{filters}") </s> remove         self._name = _get_name("upscale2x_{}_{}".format(filters, "fast" if fast else "hyb"))
 </s> add         self._name = _get_name(f"upscale2x_{filters}_{'fast' if fast else 'hyb'}")
        self._name = kwargs.pop("name") if "name" in kwargs else _get_name(f"conv_{filters}")
                   normalization=None,
                   activation="leakyrelu",
                   use_depthwise=False,
                   **kwargs):
        self._name = kwargs.pop("name") if "name" in kwargs else _get_name(f"conv_{filters}")
        self._name = kwargs.pop("name") if "name" in kwargs else _get_name(f"conv_{filters}")
  
          logger.debug("name: %s, filters: %s, kernel_size: %s, strides: %s, padding: %s, "
                       "normalization: %s, activation: %s, use_depthwise: %s, kwargs: %s)",
                       self._name, filters, kernel_size, strides, padding, normalization,
                       activation, use_depthwise, kwargs)
keep keep keep keep replace replace keep keep keep keep keep
 Update all Keras Imports to be conditional (#1214)

* Remove custom keras importer

* first round keras imports fix

* launcher.py: Remove KerasFinder references

* 2nd round keras imports update (lib and extract)

* 3rd round keras imports update (train)

* remove KerasFinder from tests

* 4th round keras imports update (tests) 
lib/model/nn_blocks.py