diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py
index 2364f70ac0c..b17a0fbe736 100644
--- a/torchrl/collectors/collectors.py
+++ b/torchrl/collectors/collectors.py
@@ -1884,28 +1884,40 @@ class MultiSyncDataCollector(_MultiDataCollector):
     trajectory and the start of the next collection.
     This class can be safely used with online RL sota-implementations.
 
+    .. note:: Python requires multiprocessed code to be instantiated within a main guard:
+
+            >>> from torchrl.collectors import MultiSyncDataCollector
+            >>> if __name__ == "__main__":
+            ...     # Create your collector here
+
+        See https://docs.python.org/3/library/multiprocessing.html for more info.
+
     Examples:
         >>> from torchrl.envs.libs.gym import GymEnv
-        >>> from torchrl.envs import StepCounter
         >>> from tensordict.nn import TensorDictModule
         >>> from torch import nn
-        >>> env_maker = lambda: TransformedEnv(GymEnv("Pendulum-v1", device="cpu"), StepCounter(max_steps=50))
-        >>> policy = TensorDictModule(nn.Linear(3, 1), in_keys=["observation"], out_keys=["action"])
-        >>> collector = MultiSyncDataCollector(
-        ...     create_env_fn=[env_maker, env_maker],
-        ...     policy=policy,
-        ...     total_frames=2000,
-        ...     max_frames_per_traj=50,
-        ...     frames_per_batch=200,
-        ...     init_random_frames=-1,
-        ...     reset_at_each_iter=False,
-        ...     devices="cpu",
-        ...     storing_devices="cpu",
-        ... )
-        >>> for i, data in enumerate(collector):
-        ...     if i == 2:
-        ...         print(data)
-        ...         break
+        >>> from torchrl.collectors import MultiSyncDataCollector
+        >>> if __name__ == "__main__":
+        ...     env_maker = lambda: GymEnv("Pendulum-v1", device="cpu")
+        ...     policy = TensorDictModule(nn.Linear(3, 1), in_keys=["observation"], out_keys=["action"])
+        ...     collector = MultiSyncDataCollector(
+        ...         create_env_fn=[env_maker, env_maker],
+        ...         policy=policy,
+        ...         total_frames=2000,
+        ...         max_frames_per_traj=50,
+        ...         frames_per_batch=200,
+        ...         init_random_frames=-1,
+        ...         reset_at_each_iter=False,
+        ...         device="cpu",
+        ...         storing_device="cpu",
+        ...         cat_results="stack",
+        ...     )
+        ...     for i, data in enumerate(collector):
+        ...         if i == 2:
+        ...             print(data)
+        ...             break
+        ... collector.shutdown()
+        ... del collector
         TensorDict(
             fields={
                 action: Tensor(shape=torch.Size([200, 1]), device=cpu, dtype=torch.float32, is_shared=False),
@@ -1932,8 +1944,6 @@ class MultiSyncDataCollector(_MultiDataCollector):
             batch_size=torch.Size([200]),
             device=cpu,
             is_shared=False)
-        >>> collector.shutdown()
-        >>> del collector
 
     """
 
@@ -1987,7 +1997,6 @@ def _queue_len(self) -> int:
         return self.num_workers
 
     def iterator(self) -> Iterator[TensorDictBase]:
-
         cat_results = self.cat_results
         if cat_results is None:
             cat_results = 0
@@ -2232,27 +2241,40 @@ class MultiaSyncDataCollector(_MultiDataCollector):
     the batch of rollouts is collected and the next call to the iterator.
     This class can be safely used with offline RL sota-implementations.
 
-    Examples:
+    .. note:: Python requires multiprocessed code to be instantiated within a main guard:
+
+            >>> from torchrl.collectors import MultiaSyncDataCollector
+            >>> if __name__ == "__main__":
+            ...     # Create your collector here
+
+        See https://docs.python.org/3/library/multiprocessing.html for more info.
+
+        Examples:
         >>> from torchrl.envs.libs.gym import GymEnv
         >>> from tensordict.nn import TensorDictModule
         >>> from torch import nn
-        >>> env_maker = lambda: GymEnv("Pendulum-v1", device="cpu")
-        >>> policy = TensorDictModule(nn.Linear(3, 1), in_keys=["observation"], out_keys=["action"])
-        >>> collector = MultiaSyncDataCollector(
-        ...     create_env_fn=[env_maker, env_maker],
-        ...     policy=policy,
-        ...     total_frames=2000,
-        ...     max_frames_per_traj=50,
-        ...     frames_per_batch=200,
-        ...     init_random_frames=-1,
-        ...     reset_at_each_iter=False,
-        ...     devices="cpu",
-        ...     storing_devices="cpu",
-        ... )
-        >>> for i, data in enumerate(collector):
-        ...     if i == 2:
-        ...         print(data)
-        ...         break
+        >>> from torchrl.collectors import MultiaSyncDataCollector
+        >>> if __name__ == "__main__":
+        ...     env_maker = lambda: GymEnv("Pendulum-v1", device="cpu")
+        ...     policy = TensorDictModule(nn.Linear(3, 1), in_keys=["observation"], out_keys=["action"])
+        ...     collector = MultiaSyncDataCollector(
+        ...         create_env_fn=[env_maker, env_maker],
+        ...         policy=policy,
+        ...         total_frames=2000,
+        ...         max_frames_per_traj=50,
+        ...         frames_per_batch=200,
+        ...         init_random_frames=-1,
+        ...         reset_at_each_iter=False,
+        ...         device="cpu",
+        ...         storing_device="cpu",
+        ...         cat_results="stack",
+        ...     )
+        ...     for i, data in enumerate(collector):
+        ...         if i == 2:
+        ...             print(data)
+        ...             break
+        ... collector.shutdown()
+        ... del collector
         TensorDict(
             fields={
                 action: Tensor(shape=torch.Size([200, 1]), device=cpu, dtype=torch.float32, is_shared=False),
@@ -2279,8 +2301,6 @@ class MultiaSyncDataCollector(_MultiDataCollector):
             batch_size=torch.Size([200]),
             device=cpu,
             is_shared=False)
-        >>> collector.shutdown()
-        >>> del collector
 
     """