diff --git a/nncf/experimental/torch/fx/nncf_graph_builder.py b/nncf/experimental/torch/fx/nncf_graph_builder.py
index ba3fed90ecb..f7501a3fd55 100644
--- a/nncf/experimental/torch/fx/nncf_graph_builder.py
+++ b/nncf/experimental/torch/fx/nncf_graph_builder.py
@@ -138,7 +138,6 @@ def get_edge_params(
         if tensor_shape is None:
             # TODO(dlyakhov): Refactor algorithms to always have knowns edges shapes.
             nncf_logger.debug(f"Edge shape between {source_node.name} and {dist_node.name} is unknown.")
-            tensor_shape = None
 
         input_port_id = dist_node.all_input_nodes.index(source_node)
         return input_port_id, output_port_id, tensor_shape
diff --git a/nncf/experimental/torch/fx/statistics/aggregator.py b/nncf/experimental/torch/fx/statistics/aggregator.py
index 7b03377b4c3..9f109147d83 100644
--- a/nncf/experimental/torch/fx/statistics/aggregator.py
+++ b/nncf/experimental/torch/fx/statistics/aggregator.py
@@ -75,8 +75,14 @@ def _get_statistic_collector_name(tp: PTTargetPoint, module_to_insert: torch.nn.
         :param module_to_insert: Given statistic collection module.
         :return: Unique statistic collector name according to given target point and module.
         """
-        tp_name = "_".join([tp.target_node_name, str(tp.input_port_id), str(tp.target_type.value)])
-        return f"{tp_name}_{str(id(module_to_insert))}"
+        return "_".join(
+            [
+                tp.target_node_name,
+                str(tp.input_port_id),
+                str(tp.target_type.value),
+                str(id(module_to_insert)),
+            ]
+        )
 
     def _get_transformation_layout_extra_outputs(
         self, statistic_points: StatisticPointsContainer