Develop fix visual torchh export (#4494)

* Fixing exporting of ONNX for visual when using threading

* docstring was wrong

ml-agents/mlagents/trainers/torch/model_serialization.py

 import os
+import threading
 from mlagents.torch_utils import torch
 
 from mlagents_envs.logging_util import get_logger
 logger = get_logger(__name__)
+
+
+class exporting_to_onnx:
+    """
+    Set this context by calling
+    ```
+    with exporting_to_onnx():
+    ```
+    Within this context, the variable exporting_to_onnx.is_exporting() will be true.
+    This implementation is thread safe.
+    """
+
+    _local_data = threading.local()
+    _local_data._is_exporting = False
+
+    def __enter__(self):
+        self._local_data._is_exporting = True
+
+    def __exit__(self, *args):
+        self._local_data._is_exporting = False
+
+    @staticmethod
+    def is_exporting():
+        if not hasattr(exporting_to_onnx._local_data, "_is_exporting"):
+            return False
+        return exporting_to_onnx._local_data._is_exporting
 
 
 class ModelSerializer:
         onnx_output_path = f"{output_filepath}.onnx"
         logger.info(f"Converting to {onnx_output_path}")
 
-        torch.onnx.export(
-            self.policy.actor_critic,
-            self.dummy_input,
-            onnx_output_path,
-            opset_version=SerializationSettings.onnx_opset,
-            input_names=self.input_names,
-            output_names=self.output_names,
-            dynamic_axes=self.dynamic_axes,
-        )
+        with exporting_to_onnx():
+            torch.onnx.export(
+                self.policy.actor_critic,
+                self.dummy_input,
+                onnx_output_path,
+                opset_version=SerializationSettings.onnx_opset,
+                input_names=self.input_names,
+                output_names=self.output_names,
+                dynamic_axes=self.dynamic_axes,
+            )
         logger.info(f"Exported {onnx_output_path}")

ml-agents/mlagents/trainers/torch/networks.py

 from mlagents.trainers.torch.utils import ModelUtils
 from mlagents.trainers.torch.decoders import ValueHeads
 from mlagents.trainers.torch.layers import LSTM, LinearEncoder
+from mlagents.trainers.torch.model_serialization import exporting_to_onnx
 
 ActivationFunction = Callable[[torch.Tensor], torch.Tensor]
 EncoderFunction = Callable[
 
         for idx, processor in enumerate(self.visual_processors):
             vis_input = vis_inputs[idx]
-            if not torch.onnx.is_in_onnx_export():
+            if not exporting_to_onnx.is_exporting():
                 vis_input = vis_input.permute([0, 3, 1, 2])
             processed_vis = processor(vis_input)
             encodes.append(processed_vis)