          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 ""),
                       **self._kwargs)(inputs)
          # normalization
          if self._normalization == "instance":
              var_x = InstanceNormalization(name="{}_instancenorm".format(self._name))(var_x)
          if self._normalization == "batch":
 
</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".format(self._name))(inputs)
 </s> add                                          name=f"{self._name}_reflectionpadding2d")(inputs) </s> remove             var_x = BatchNormalization(axis=3, name="{}_batchnorm".format(self._name))(var_x)
 </s> add             var_x = BatchNormalization(axis=3, name=f"{self._name}_batchnorm")(var_x) </s> remove                                 name="{}_seperableconv2d".format(self._name),
 </s> add                                 name=f"{self._name}_seperableconv2d", </s> remove         var_x = Activation("relu", name="{}_relu".format(self._name))(var_x)
 </s> add         var_x = Activation("relu", name=f"{self._name}_relu")(var_x)
                     name=f"{self._name}_{'dw' if self._use_depthwise else ''}conv2d",
          conv = DepthwiseConv2D if self._use_depthwise else Conv2D
          var_x = conv(*self._args,
                       strides=self._strides,
                       padding=self._padding,
                     name=f"{self._name}_{'dw' if self._use_depthwise else ''}conv2d",
                       **self._kwargs)(inputs)
          # normalization
          if self._normalization == "instance":
              var_x = InstanceNormalization(name="{}_instancenorm".format(self._name))(var_x)
          if self._normalization == "batch":
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