                                      **self._kwargs)(var_x)
          if self._fast or (not self._fast and self._filters > 0):
              var_x2 = Conv2D(self._filters, 3,
                              padding=self._padding,
                              name="{}_conv2d".format(self._name),
                              **self._kwargs)(var_x)
              var_x2 = UpSampling2D(size=(self._scale_factor, self._scale_factor),
                                    interpolation=self._interpolation,
                                    name="{}_upsampling2D".format(self._name))(var_x2)
              if self._fast:
 
</s> remove                                   name="{}_upsampling2D".format(self._name))(var_x2)
 </s> add                                   name=f"{self._name}_upsampling2D")(var_x2) </s> remove                                  name="{}_resize".format(self._name))(var_x)
 </s> add                                  name=f"{self._name}_resize")(var_x) </s> remove                                         name="{}_reflectionpadding2d_0".format(self._name))(var_x)
 </s> add                                         name=f"{self._name}_reflectionpadding2d_0")(var_x) </s> remove                        name="{}_conv2d_0".format(self._name),
 </s> add                        name=f"{self._name}_conv2d_0", </s> remove             outputs.append(Conv2DOutput(3, 5, name="face_out_32_{}".format(side))(var_x1))
 </s> add             outputs.append(Conv2DOutput(3, 5, name=f"face_out_32_{side}")(var_x1))
                            name=f"{self._name}_conv2d",
                                      **self._kwargs)(var_x)
          if self._fast or (not self._fast and self._filters > 0):
              var_x2 = Conv2D(self._filters, 3,
                              padding=self._padding,
                            name=f"{self._name}_conv2d",
                              **self._kwargs)(var_x)
              var_x2 = UpSampling2D(size=(self._scale_factor, self._scale_factor),
                                    interpolation=self._interpolation,
                                    name="{}_upsampling2D".format(self._name))(var_x2)
              if self._fast:
keep keep keep keep 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