diff --git a/converter/src/torchconverter/tracer.py b/converter/src/torchconverter/tracer.py
index b970c8d..340ebbf 100644
--- a/converter/src/torchconverter/tracer.py
+++ b/converter/src/torchconverter/tracer.py
@@ -131,7 +131,7 @@ def print_graph(self):
         """
         self.gm.graph.print_tabular()
 
-    def get_modules_from_sequential(self, module: torch.nn.Sequential, indicies: List[int]) -> torch.nn.Module:
+    def _get_inner_module(self, module: torch.nn.Module, target_hierarchy: List[str]) -> torch.nn.Module:
         """
         Get a module in a nn.Sequential layer.
         This function will recursively unpack the nn.Sequential layers to get the innermost module.
@@ -143,9 +143,14 @@ def get_modules_from_sequential(self, module: torch.nn.Sequential, indicies: Lis
         Returns:
             The innermost module.
         """
-        if len(indicies) == 0:
-            return module
-        return self.get_modules_from_sequential(module[indicies[0]], indicies[1:])
+        module_name = target_hierarchy[0]
+        target_hierarchy = target_hierarchy[1:]
+        submodule = getattr(module, module_name)
+
+        if len(target_hierarchy) == 0:
+            return submodule
+        
+        return self._get_inner_module(submodule, target_hierarchy)
 
     def get_module(self, module_name: str) -> torch.nn.Module:
         """
@@ -161,13 +166,7 @@ def get_module(self, module_name: str) -> torch.nn.Module:
         if "." in module_name:
             # if we have nn.Sequential layers
             target_hierarchy = module_name.split(".")
-            sequential_name = target_hierarchy[0]
-
-            # indicies = target_hierarchy[1:]
-            indicies = [int(x) for x in target_hierarchy[1:]]
-
-            module = getattr(self.model, sequential_name)
-            return self.get_modules_from_sequential(module, indicies)
+            return self._get_inner_module(self.model, target_hierarchy)
         
         return getattr(self.model, module_name)