coremltools/converters/keras/_topology.py [230:273]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                h_in_name = layer + "_h_in"
                h_out_name = layer + "_h_out"
                self.optional_inputs.append((h_in_name, hidden_size))
                self.optional_outputs.append((h_out_name, hidden_size))
                _insert_to_dict(self.layers_optional_inputs, layer, h_in_name)
                _insert_to_dict(self.layers_optional_outputs, layer, h_out_name)
                if isinstance(keras_layer, _keras.layers.recurrent.LSTM):
                    c_in_name = layer + "_c_in"
                    c_out_name = layer + "_c_out"
                    self.optional_inputs.append((c_in_name, hidden_size))
                    self.optional_outputs.append((c_out_name, hidden_size))
                    _insert_to_dict(self.layers_optional_inputs, layer, c_in_name)
                    _insert_to_dict(self.layers_optional_outputs, layer, c_out_name)
                elif isinstance(keras_layer, _keras.layers.wrappers.Bidirectional):
                    c_in_name = layer + "_c_in"
                    c_out_name = layer + "_c_out"
                    h_in_name_rev = layer + "_h_in_rev"
                    c_in_name_rev = layer + "_c_in_rev"
                    h_out_name_rev = layer + "_h_out_rev"
                    c_out_name_rev = layer + "_c_out_rev"
                    self.optional_inputs.append((c_in_name, hidden_size))
                    self.optional_outputs.append((c_out_name, hidden_size))
                    self.optional_inputs.append((h_in_name_rev, hidden_size))
                    self.optional_inputs.append((c_in_name_rev, hidden_size))
                    self.optional_outputs.append((h_out_name_rev, hidden_size))
                    self.optional_outputs.append((c_out_name_rev, hidden_size))
                    _insert_to_dict(self.layers_optional_inputs, layer, c_in_name)
                    _insert_to_dict(self.layers_optional_outputs, layer, c_out_name)
                    _insert_to_dict(self.layers_optional_inputs, layer, h_in_name_rev)
                    _insert_to_dict(self.layers_optional_inputs, layer, c_in_name_rev)
                    _insert_to_dict(self.layers_optional_outputs, layer, h_out_name_rev)
                    _insert_to_dict(self.layers_optional_outputs, layer, c_out_name_rev)

    def _get_first_embedded_model(self):
        for idx, layer in enumerate(self.layer_list):
            keras_layer = self.keras_layer_map[layer]
            if isinstance(keras_layer, _keras.models.Sequential) or isinstance(
                keras_layer, _keras.models.Model
            ):
                return idx
        return -1

    def _get_first_shared_layer(self):
        for idx, layer in enumerate(self.layer_list):
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



coremltools/converters/keras/_topology2.py [308:351]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                h_in_name = layer + "_h_in"
                h_out_name = layer + "_h_out"
                self.optional_inputs.append((h_in_name, hidden_size))
                self.optional_outputs.append((h_out_name, hidden_size))
                _insert_to_dict(self.layers_optional_inputs, layer, h_in_name)
                _insert_to_dict(self.layers_optional_outputs, layer, h_out_name)
                if isinstance(keras_layer, _keras.layers.recurrent.LSTM):
                    c_in_name = layer + "_c_in"
                    c_out_name = layer + "_c_out"
                    self.optional_inputs.append((c_in_name, hidden_size))
                    self.optional_outputs.append((c_out_name, hidden_size))
                    _insert_to_dict(self.layers_optional_inputs, layer, c_in_name)
                    _insert_to_dict(self.layers_optional_outputs, layer, c_out_name)
                elif isinstance(keras_layer, _keras.layers.wrappers.Bidirectional):
                    c_in_name = layer + "_c_in"
                    c_out_name = layer + "_c_out"
                    h_in_name_rev = layer + "_h_in_rev"
                    c_in_name_rev = layer + "_c_in_rev"
                    h_out_name_rev = layer + "_h_out_rev"
                    c_out_name_rev = layer + "_c_out_rev"
                    self.optional_inputs.append((c_in_name, hidden_size))
                    self.optional_outputs.append((c_out_name, hidden_size))
                    self.optional_inputs.append((h_in_name_rev, hidden_size))
                    self.optional_inputs.append((c_in_name_rev, hidden_size))
                    self.optional_outputs.append((h_out_name_rev, hidden_size))
                    self.optional_outputs.append((c_out_name_rev, hidden_size))
                    _insert_to_dict(self.layers_optional_inputs, layer, c_in_name)
                    _insert_to_dict(self.layers_optional_outputs, layer, c_out_name)
                    _insert_to_dict(self.layers_optional_inputs, layer, h_in_name_rev)
                    _insert_to_dict(self.layers_optional_inputs, layer, c_in_name_rev)
                    _insert_to_dict(self.layers_optional_outputs, layer, h_out_name_rev)
                    _insert_to_dict(self.layers_optional_outputs, layer, c_out_name_rev)

    def _get_first_embedded_model(self):
        for idx, layer in enumerate(self.layer_list):
            keras_layer = self.keras_layer_map[layer]
            if isinstance(keras_layer, _keras.models.Sequential) or isinstance(
                keras_layer, _keras.models.Model
            ):
                return idx
        return -1

    def _get_first_shared_layer(self):
        for idx, layer in enumerate(self.layer_list):
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -