diff --git a/torchrec/torchrec_embcache/src/torchrec_embcache/distributed/embedding_bag.py b/torchrec/torchrec_embcache/src/torchrec_embcache/distributed/embedding_bag.py
index 76d47d710797616e3544580f0a4d6a171fb8307c..f397fe72545ce0cdbd206535ebf3a35f72b58886 100644
--- a/torchrec/torchrec_embcache/src/torchrec_embcache/distributed/embedding_bag.py
+++ b/torchrec/torchrec_embcache/src/torchrec_embcache/distributed/embedding_bag.py
@@ -172,40 +172,6 @@ class EmbCacheEmbeddingBagCollection(EmbeddingBagCollection):
         tables (List[EmbeddingBagConfig]): list of embedding tables.
         is_weighted (bool): whether input `KeyedJaggedTensor` is weighted.
         device (Optional[torch.device]): default compute device.
-
-    Example::
-
-        table_0 = EmbeddingBagConfig(
-            name="t1", embedding_dim=3, num_embeddings=10, feature_names=["f1"]
-        )
-        table_1 = EmbeddingBagConfig(
-            name="t2", embedding_dim=4, num_embeddings=10, feature_names=["f2"]
-        )
-
-        ebc = EmbeddingBagCollection(tables=[table_0, table_1])
-
-        #        0       1        2  <-- batch
-        # "f1"   [0,1] None    [2]
-        # "f2"   [3]    [4]    [5,6,7]
-        #  ^
-        # feature
-
-        features = KeyedJaggedTensor(
-            keys=["f1", "f2"],
-            values=torch.tensor([0, 1, 2, 3, 4, 5, 6, 7]),
-            offsets=torch.tensor([0, 2, 2, 3, 4, 5, 8]),
-        )
-
-        pooled_embeddings = ebc(features)
-        print(pooled_embeddings.values())
-        tensor([[-0.8899, -0.1342, -1.9060, -0.0905, -0.2814, -0.9369, -0.7783],
-            [ 0.0000,  0.0000,  0.0000,  0.1598,  0.0695,  1.3265, -0.1011],
-            [-0.4256, -1.1846, -2.1648, -1.0893,  0.3590, -1.9784, -0.7681]],
-            grad_fn=<CatBackward0>)
-        print(pooled_embeddings.keys())
-        ['f1', 'f2']
-        print(pooled_embeddings.offset_per_key())
-        tensor([0, 3, 7])
     """
 
     def __init__(
diff --git a/torchrec/torchrec_embcache/src/torchrec_embcache/distributed/train_pipeline.py b/torchrec/torchrec_embcache/src/torchrec_embcache/distributed/train_pipeline.py
index 8651ab84c0495bc6dde45da692b07dbee1149895..2d670aecf2c34cecda8142245445abf7e450ba44 100644
--- a/torchrec/torchrec_embcache/src/torchrec_embcache/distributed/train_pipeline.py
+++ b/torchrec/torchrec_embcache/src/torchrec_embcache/distributed/train_pipeline.py
@@ -120,7 +120,6 @@ class EmbCacheTrainPipelineContext(TrainPipelineContext):
 
 
 class EmbCachePipelinedForward(PipelinedForward):
-    # pyre-ignore [2, 24]
     def __call__(self, *input_feature, **kwargs) -> Awaitable:
         self._context.sparse_features_after_restore_future.pop(self._name).get()
         data = self._context.sparse_features_after_post_dist.pop(self._name)
@@ -216,7 +215,6 @@ def _fuse_input_dist_splits(context: TrainPipelineContext) -> None:
                 (
                     names,
                     FusedKJTListSplitsAwaitable(
-                        # pyre-ignore[6]
                         requests=[
                             context.input_dist_splits_requests[name] for name in names
                         ],
@@ -603,7 +601,6 @@ class EmbCacheTrainPipelineSparseDist(TrainPipelineSparseDist[In, Out]):
             return
 
         self._init_pipelined_modules(
-            # pyre-ignore [6]
             self.batches[0],
             self.contexts[0],
             EmbCachePipelinedForward,