          """
          if self._use_reflect_padding:
              inputs = ReflectionPadding2D(stride=self._strides,
                                           kernel_size=self._args[-1],
                                           name="{}_reflectionpadding2d".format(self._name))(inputs)
          conv = DepthwiseConv2D if self._use_depthwise else Conv2D
          var_x = conv(*self._args,
                       strides=self._strides,
                       padding=self._padding,
                       name="{}_{}conv2d".format(self._name, "dw" if self._use_depthwise else ""),
 
</s> remove                      name="{}_{}conv2d".format(self._name, "dw" if self._use_depthwise else ""),
 </s> add                      name=f"{self._name}_{'dw' if self._use_depthwise else ''}conv2d", </s> remove             var_x = InstanceNormalization(name="{}_instancenorm".format(self._name))(var_x)
 </s> add             var_x = InstanceNormalization(name=f"{self._name}_instancenorm")(var_x) </s> remove                                         name="{}_reflectionpadding2d_0".format(self._name))(var_x)
 </s> add                                         name=f"{self._name}_reflectionpadding2d_0")(var_x) </s> remove             rec_name = "{}_{}".format(name, i)
 </s> add             rec_name = f"{name}_{i}" </s> remove                        name="{}_conv2d_0".format(self._name),
 </s> add                        name=f"{self._name}_conv2d_0",
                                         name=f"{self._name}_reflectionpadding2d")(inputs)
          """
          if self._use_reflect_padding:
              inputs = ReflectionPadding2D(stride=self._strides,
                                           kernel_size=self._args[-1],
                                         name=f"{self._name}_reflectionpadding2d")(inputs)
          conv = DepthwiseConv2D if self._use_depthwise else Conv2D
          var_x = conv(*self._args,
                       strides=self._strides,
                       padding=self._padding,
                       name="{}_{}conv2d".format(self._name, "dw" if self._use_depthwise else ""),
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