fix test torch distributions

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

     optimizer = torch.optim.Adam(gauss_dist.parameters(), lr=3e-3)
 
     for _ in range(50):
-        dist_inst = gauss_dist(sample_embedding)[0]
+        dist_inst = gauss_dist(sample_embedding)
         if tanh_squash:
             assert isinstance(dist_inst, TanhGaussianDistInstance)
         else:

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

         discrete_dist: Optional[List[DiscreteDistInstance]] = None
         # This checks None because mypy complains otherwise
         if self._continuous_distribution is not None:
-            continuous_dist = self._continuous_distribution(inputs, masks)
+            continuous_dist = self._continuous_distribution(inputs)
         if self._discrete_distribution is not None:
             discrete_dist = self._discrete_distribution(inputs, masks)
         return DistInstances(continuous_dist, discrete_dist)

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

                 torch.zeros(1, num_outputs, requires_grad=True)
             )
 
-    def forward(self, inputs: torch.Tensor, masks: torch.Tensor) -> List[DistInstance]:
+    def forward(self, inputs: torch.Tensor) -> List[DistInstance]:
         mu = self.mu(inputs)
         if self.conditional_sigma:
             log_sigma = torch.clamp(self.log_sigma(inputs), min=-20, max=2)