Fix Pyre missing annotations in test_disjoint_bandit_container.py

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This diff fixes pyre-missing-annotations warnings identified by Quality Insight from [Monetization codehub](https://fburl.com/quality/0kw8oby2)
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## Summary:
Fixed pyre-missing-annotations warnings in `fbcode/pearl/test/unit/with_pytorch/test_disjoint_bandit_container.py` by adding type annotations to the `PolicyLearnerWrapper` class methods:

1. Added `DisjointBanditContainer` and `ActionSpace` type annotations to the `__init__` method parameters and `-> None` return type
2. Added `torch.Tensor` type annotation to the `forward` method parameter and `-> Action` return type
3. Added necessary imports for `Action` and `ActionSpace` types

The changes are minimal and only add the missing type annotations without modifying any functionality or logic.
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Differential Revision: D82289854

fbshipit-source-id: 3eea08caedee6e41003f36ba1f0e3994d01f38c3

Author: generatedunixname893464919433493

test/unit/with_pytorch/test_disjoint_bandit_container.py

@@ -14,6 +14,8 @@
 import torch.jit
 import torch.testing as tt
 from parameterized import parameterized_class
+from pearl.api.action import Action
+from pearl.api.action_space import ActionSpace
 from pearl.policy_learners.contextual_bandits.disjoint_bandit import (
     DisjointBanditContainer,
 )
@@ -294,12 +296,14 @@ def test_traced_disjoint_bandit_container(self) -> None:
 
         # Define a wrapper function for tracing that handles the act method
         class PolicyLearnerWrapper(torch.nn.Module):
-            def __init__(self, policy_learner, action_space):
+            def __init__(
+                self, policy_learner: DisjointBanditContainer, action_space: ActionSpace
+            ) -> None:
                 super().__init__()
                 self.policy_learner = policy_learner
                 self.action_space = action_space
 
-            def forward(self, state):
+            def forward(self, state: torch.Tensor) -> Action:
                 return self.policy_learner.act(
                     subjective_state=state, available_action_space=self.action_space
                 )