[Codemod][FBSourceBlackLinter] Daily `arc lint --take BLACK`

Reviewed By: zertosh

Differential Revision: D33347867

fbshipit-source-id: 7672f65392e363c0359de2d86e745782a09cf9dc

examples/asr/librispeech_emformer_rnnt/pipeline_demo.py

-from argparse import ArgumentParser
 import pathlib
+from argparse import ArgumentParser
+
 import torch
 import torchaudio
 from torchaudio.prototype.rnnt_pipeline import EMFORMER_RNNT_BASE_LIBRISPEECH
@@ -8,7 +9,10 @@ from torchaudio.prototype.rnnt_pipeline import EMFORMER_RNNT_BASE_LIBRISPEECH
 def cli_main():
     parser = ArgumentParser()
     parser.add_argument(
-        "--librispeech_path", type=pathlib.Path, required=True, help="Path to LibriSpeech datasets.",
+        "--librispeech_path",
+        type=pathlib.Path,
+        required=True,
+        help="Path to LibriSpeech datasets.",
     )
     args = parser.parse_args()
 
@@ -31,7 +35,7 @@ def cli_main():
         # Streaming decode.
         state, hypothesis = None, None
         for idx in range(0, len(waveform), num_samples_segment):
-            segment = waveform[idx: idx + num_samples_segment_right_context]
+            segment = waveform[idx : idx + num_samples_segment_right_context]
             segment = torch.nn.functional.pad(segment, (0, num_samples_segment_right_context - len(segment)))
             with torch.no_grad():
                 features, length = streaming_feature_extractor(segment)

torchaudio/prototype/rnnt_pipeline.py

-from abc import ABC, abstractmethod
-from typing import Callable, List, Tuple
-from dataclasses import dataclass
 import json
 import math
 import pathlib
-import torch
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Callable, List, Tuple
 
+import torch
 import torchaudio
 from torchaudio._internal import download_url_to_file, load_state_dict_from_url, module_utils
 from torchaudio.prototype import RNNT, RNNTBeamSearch, emformer_rnnt_base