[CodeQuality] call all() without making a list

test/test_tensor_spec.py

@@ -972,13 +972,13 @@ def test_discrete_action_spec_reconstruct(self, action_spec_cls):
         actions_numpy = [action_spec.to_numpy(a) for a in actions_tensors]
         actions_tensors_2 = [action_spec.encode(a) for a in actions_numpy]
         assert all(
-            [(a1 == a2).all() for a1, a2 in zip(actions_tensors, actions_tensors_2)]
+            (a1 == a2).all() for a1, a2 in zip(actions_tensors, actions_tensors_2)
         )
 
         actions_numpy = [int(np.random.randint(0, 10, (1,))) for a in actions_tensors]
         actions_tensors = [action_spec.encode(a) for a in actions_numpy]
         actions_numpy_2 = [action_spec.to_numpy(a) for a in actions_tensors]
-        assert all([(a1 == a2) for a1, a2 in zip(actions_numpy, actions_numpy_2)])
+        assert all((a1 == a2) for a1, a2 in zip(actions_numpy, actions_numpy_2))
 
     def test_mult_discrete_action_spec_reconstruct(self):
         torch.manual_seed(0)
@@ -999,7 +999,7 @@ def test_mult_discrete_action_spec_reconstruct(self):
         ]
         actions_tensors = [action_spec.encode(a) for a in actions_numpy]
         actions_numpy_2 = [action_spec.to_numpy(a) for a in actions_tensors]
-        assert all([(a1 == a2).all() for a1, a2 in zip(actions_numpy, actions_numpy_2)])
+        assert all((a1 == a2).all() for a1, a2 in zip(actions_numpy, actions_numpy_2))
 
     def test_one_hot_discrete_action_spec_rand(self):
         torch.manual_seed(0)

test/test_trainer.py

@@ -456,8 +456,8 @@ def make_storage():
                 )  # trainer.app_state["state"]["replay_buffer.replay_buffer._storage._storage"]
                 td2 = trainer2._modules["replay_buffer"].replay_buffer._storage._storage
                 if storage_type == "list":
-                    assert all([(_td1 == _td2).all() for _td1, _td2 in zip(td1, td2)])
-                    assert all([(_td1 is not _td2) for _td1, _td2 in zip(td1, td2)])
+                    assert all((_td1 == _td2).all() for _td1, _td2 in zip(td1, td2))
+                    assert all((_td1 is not _td2) for _td1, _td2 in zip(td1, td2))
                     assert storage2._storage is td2
                 else:
                     assert (td1 == td2).all()

torchrl/envs/transforms/transforms.py

@@ -1792,7 +1792,7 @@ def _call(self, tensordict: TensorDictBase) -> TensorDictBase:
             self.in_keys = self._find_in_keys()
             self._initialized = True
 
-        if all([key in tensordict.keys(include_nested=True) for key in self.in_keys]):
+        if all(key in tensordict.keys(include_nested=True) for key in self.in_keys):
             values = [tensordict.get(key) for key in self.in_keys]
             if self.unsqueeze_if_oor:
                 pos_idx = self.dim > 0
@@ -2560,7 +2560,7 @@ def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec
         if isinstance(reward_spec, CompositeSpec):
 
             # If reward_spec is a CompositeSpec, all in_keys should be keys of reward_spec
-            if not all([k in reward_spec.keys() for k in self.in_keys]):
+            if not all(k in reward_spec.keys() for k in self.in_keys):
                 raise KeyError("Not all in_keys are present in ´reward_spec´")
 
             # Define episode specs for all out_keys