fixed numerical condition for pytorch models and updates to type hints

hls4ml/model/profiling.py

@@ -414,7 +414,7 @@ def _register_layer(self, name: str) -> None:
     def _is_parameterized(self, module: torch.nn.Module) -> bool:
         return any(p.requires_grad for p in module.parameters())
 
-    def _get_weights(self) -> pandas.DataFrame:
+    def _get_weights(self) -> pandas.DataFrame | list[dict]:
         suffix = ['w', 'b']
         if self.fmt == 'longform':
             data = {'x': [], 'layer': [], 'weight': []}
@@ -446,9 +446,12 @@ def _get_weights(self) -> pandas.DataFrame:
             data = pandas.DataFrame(data)
         return data
 
-    def get_weights(self) -> dict:
+    def get_weights(self) -> pandas.DataFrame | list[dict]:
         return self._get_weights()
 
+    def get_layers(self) -> list[str]:
+        return self.registered_layers
+
     def _get_layer(self, layer_name: str, module: torch.nn.Module) -> torch.nn.Module:
         for name in layer_name.split('.')[1:]:
             module = getattr(module, name)
@@ -526,11 +529,11 @@ def numerical(model=None, hls_model=None, X=None, plot='boxplot'):
     elif model_present:
         if __tf_profiling_enabled__ and isinstance(model, keras.Model):
             data = weights_keras(model, fmt='summary', plot=plot)
-        elif __torch_profiling_enabled__ and isinstance(model, torch.nn.Sequential):
+        elif __torch_profiling_enabled__ and isinstance(model, torch.nn.Module):
             data = weights_torch(model, fmt='summary', plot=plot)
 
     if data is None:
-        print("Only keras, PyTorch (Sequential) and ModelGraph models " + "can currently be profiled")
+        print("Only keras, PyTorch and ModelGraph models " + "can currently be profiled")
 
         if hls_model_present and os.path.exists(tmp_output_dir):
             shutil.rmtree(tmp_output_dir)