suppress errors in reagent

Reviewed By: connernilsen

Differential Revision: D53101150

fbshipit-source-id: 8aa3785abe2af77b304dd0cd1f4605f0f9b24dec

Author: generatedunixname89002005307016

reagent/ope/estimators/slate_estimators.py

@@ -538,6 +538,7 @@ def slate_space(
                 if max_size <= 0 or max_size > len(next_buffer):
                     buffer = next_buffer
                 else:
+                    # pyre-fixme[20]: Argument `k` expected.
                     buffer = random.sample(next_buffer, max_size)
             return [([SlateItem(i) for i in b[0]], b[1]) for b in buffer]
 

reagent/ope/estimators/types.py

@@ -28,16 +28,12 @@ class TypeWrapper(Generic[ValueType]):
 
     def __index__(self) -> int:
         try:
-            # pyre-fixme[6]: For 1st param expected `Union[_SupportsTrunc, bytes,
-            #  str, SupportsInt, SupportsIndex]` but got `ValueType`.
             return int(self.value)
         except Exception:
             raise ValueError(f"{self} cannot be used as index")
 
     def __int__(self) -> int:
         try:
-            # pyre-fixme[6]: For 1st param expected `Union[_SupportsTrunc, bytes,
-            #  str, SupportsInt, SupportsIndex]` but got `ValueType`.
             return int(self.value)
         except Exception:
             raise ValueError(f"{self} cannot be converted to int")
@@ -172,8 +168,6 @@ def _values_copy(self) -> Sequence[ValueType]:
     def index_of(self, key: KeyType) -> int:
         if self._key_to_index is None:
             try:
-                # pyre-fixme[6]: Expected `Union[_SupportsIndex, bytes, str,
-                #  typing.SupportsInt]` for 1st param but got `KeyType`.
                 index = int(key)
                 if 0 <= index < len(self):
                     return index

reagent/ope/test/multiclass_bandits.py

@@ -135,11 +135,13 @@ def train_val_test_split(
     ):
         total_len = len(self._data_frame)
         train_len = int(total_len * ratios[0])
+        # pyre-fixme[20]: Argument `k` expected.
         train_choices = random.sample(range(total_len), train_len)
         train_x = np.take(self._features, train_choices, axis=0)
         train_y = np.take(self._class_indices, train_choices)
         train_r = np.take(self._one_hots, train_choices, axis=0)
         fit_len = int(train_len * ratios[1])
+        # pyre-fixme[20]: Argument `k` expected.
         fit_choices = random.sample(range(train_len), fit_len)
         fit_x = np.take(train_x, fit_choices, axis=0)
         fit_y = np.take(train_y, fit_choices)
@@ -260,6 +262,7 @@ def evaluate_all(
     for estimators, num_samples in experiments:
         samples = []
         for _ in range(num_samples):
+            # pyre-fixme[20]: Argument `k` expected.
             qid = random.sample(test_queries, 1)
             label = int(dataset.labels[qid].item())
             log_action, log_action_probabilities = log_policy(qid)