Fix of the test for multi visual input

ml-agents/mlagents/trainers/policy/policy.py

         self.vis_obs_size = sum(
             1 for shape in behavior_spec.observation_shapes if len(shape) == 3
         )
-        self.vis_obs_shape = (
-            [shape for shape in behavior_spec.observation_shapes if len(shape) == 3][0]
-            if self.vis_obs_size > 0
-            else None
-        )
         self.use_continuous_act = behavior_spec.is_action_continuous()
         self.num_branches = self.behavior_spec.action_size
         self.previous_action_dict: Dict[str, np.array] = {}

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

         self.policy = policy
         batch_dim = [1]
         dummy_vec_obs = [torch.zeros(batch_dim + [self.policy.vec_obs_size])]
-        dummy_vis_obs = (
-            [torch.zeros(batch_dim + list(self.policy.vis_obs_shape))]
-            if self.policy.vis_obs_size > 0
-            else []
-        )
+        dummy_vis_obs = [
+            torch.zeros(batch_dim + list(shape))
+            for shape in self.policy.behavior_spec.observation_shapes
+            if len(shape) == 3
+        ]
-        # Need to pass all posslible inputs since currently keyword arguments is not
+        # Need to pass all possible inputs since currently keyword arguments is not
         # supported by torch.nn.export()
         self.dummy_input = (dummy_vec_obs, dummy_vis_obs, dummy_masks, dummy_memories)
 
         if self.policy.use_vec_obs:
             self.input_names.append("vector_observation")
             self.dynamic_axes.update({"vector_observation": {0: "batch"}})
-        if self.policy.use_vis_obs:
-            self.input_names.append("visual_observation")
-            self.dynamic_axes.update({"visual_observation": {0: "batch"}})
+        for i in range(self.policy.vis_obs_size):
+            self.input_names.append(f"visual_observation_{i}")
+            self.dynamic_axes.update({f"visual_observation_{i}": {0: "batch"}})
         if not self.policy.use_continuous_act:
             self.input_names.append("action_masks")
             self.dynamic_axes.update({"action_masks": {0: "batch"}})