diff --git a/stable_baselines3/common/save_util.py b/stable_baselines3/common/save_util.py
index 7260f11..8eab899 100644
--- a/stable_baselines3/common/save_util.py
+++ b/stable_baselines3/common/save_util.py
@@ -404,7 +404,7 @@ def load_from_zip_file(
                     # Load the parameters with the right ``map_location``.
                     # Remove ".pth" ending with splitext
                     th_object = th.load(file_content, map_location=device)
-                    if file_path == "pytorch_variables.pth":
+                    if file_path == "pytorch_variables.pth" or file_path == "tensors.pth":
                         # PyTorch variables (not state_dicts)
                         pytorch_variables = th_object
                     else:
diff --git a/stable_baselines3/tqc/tqc.py b/stable_baselines3/tqc/tqc.py
index 34f370b..750c97b 100644
--- a/stable_baselines3/tqc/tqc.py
+++ b/stable_baselines3/tqc/tqc.py
@@ -468,7 +468,7 @@ class TQC(OffPolicyAlgorithm):
             reset_num_timesteps=reset_num_timesteps,
         )
 
-    def excluded_save_params(self) -> List[str]:
+    def _excluded_save_params(self) -> List[str]:
         """
         Returns the names of the parameters that should be excluded by default
         when saving the model.
@@ -478,14 +478,14 @@ class TQC(OffPolicyAlgorithm):
         # Exclude aliases
         return super(TQC, self).excluded_save_params() + ["actor", "critic", "critic_target"]
 
-    def get_torch_variables(self) -> Tuple[List[str], List[str]]:
+    def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
         """
         cf base class
         """
         state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
-        saved_tensors = ["log_ent_coef"]
+        saved_pytorch_variables = ["log_ent_coef"]
         if self.ent_coef_optimizer is not None:
             state_dicts.append("ent_coef_optimizer")
         else:
-            saved_tensors.append("ent_coef_tensor")
-        return state_dicts, saved_tensors
+            saved_pytorch_variables.append("ent_coef_tensor")
+        return state_dicts, saved_pytorch_variables