                                   name="{}_resize".format(self._name))(var_x)
          var_x_sr = Conv2D(self._filters, self._kernel_size,
                            strides=1,
                            padding=self._padding,
                            name="{}_conv".format(self._name))(var_x_sr)
          var_x_us = Conv2DTranspose(self._filters, 3,
                                     strides=2,
                                     padding=self._padding,
                                     name="{}_convtrans".format(self._name))(var_x)
          var_x = Add()([var_x_sr, var_x_us])
 
</s> remove                                  name="{}_resize".format(self._name))(var_x)
 </s> add                                  name=f"{self._name}_resize")(var_x) </s> remove                                    name="{}_convtrans".format(self._name))(var_x)
 </s> add                                    name=f"{self._name}_convtrans")(var_x) </s> remove                        name="{}_conv2d".format(self._name),
 </s> add                        name=f"{self._name}_conv2d", </s> remove             var_x = LeakyReLU(0.2, name="{}_leakyrelu".format(self._name))(var_x)
 </s> add             var_x = LeakyReLU(0.2, name=f"{self._name}_leakyrelu")(var_x) </s> remove                                         name="{}_reflectionpadding2d_0".format(self._name))(var_x)
 </s> add                                         name=f"{self._name}_reflectionpadding2d_0")(var_x)
                          name=f"{self._name}_conv")(var_x_sr)
                                   name="{}_resize".format(self._name))(var_x)
          var_x_sr = Conv2D(self._filters, self._kernel_size,
                            strides=1,
                            padding=self._padding,
                          name=f"{self._name}_conv")(var_x_sr)
          var_x_us = Conv2DTranspose(self._filters, 3,
                                     strides=2,
                                     padding=self._padding,
                                     name="{}_convtrans".format(self._name))(var_x)
          var_x = Add()([var_x_sr, var_x_us])
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