fix imports in test utils

ml-agents-envs/mlagents_envs/base_env.py

         for the correct number of agents and ensures the type.
         """
         _expected_shape = (n_agents, self.continuous_size)
-        if self.continuous_size > 0 and actions.continuous.shape != _expected_shape:
+        if actions.continuous.shape != _expected_shape:
             raise UnityActionException(
                 f"The behavior {name} needs a continuous input of dimension "
                 f"{_expected_shape} for (<number of agents>, <action size>) but "
-        if self.discrete_size > 0 and actions.discrete.shape != _expected_shape:
+        if actions.discrete.shape != _expected_shape:
             raise UnityActionException(
                 f"The behavior {name} needs a discrete input of dimension "
                 f"{_expected_shape} for (<number of agents>, <action size>) but "

ml-agents/mlagents/trainers/subprocess_env_manager.py

             if req.cmd == EnvironmentCommand.STEP:
                 all_action_info = req.payload
                 for brain_name, action_info in all_action_info.items():
-                    if len(action_info.action) != 0:
+                    if (
+                        len(action_info.action.continuous) > 0
+                        or len(action_info.action.discrete) > 0
+                    ):
                         env.set_actions(brain_name, action_info.action)
                 env.step()
                 all_step_result = _generate_all_results()

ml-agents/mlagents/trainers/tests/torch/test_utils.py

 from mlagents.trainers.torch.utils import ModelUtils
 from mlagents.trainers.exception import UnityTrainerException
 from mlagents.trainers.torch.encoders import VectorInput
-from mlagents.trainers.torch.distributions import (
-    CategoricalDistInstance,
-    GaussianDistInstance,
-)
 
 
 def test_min_visual_size():