Refactor coding style for wavernn example (#1663)

examples/pipeline_wavernn/datasets.py

-import os
 import random
 
 import torch
-import torchaudio
 from torch.utils.data.dataset import random_split
 from torchaudio.datasets import LJSPEECH, LIBRITTS
 from torchaudio.transforms import MuLawEncoding

examples/pipeline_wavernn/inference.py

 import argparse
 
 import torch
-import torch.nn.functional as F
 import torchaudio
 from torchaudio.transforms import MelSpectrogram
 from torchaudio.models import wavernn

examples/pipeline_wavernn/main.py

 import argparse
 import logging
 import os
-import signal
 from collections import defaultdict
 from datetime import datetime
 from time import time
@@ -9,7 +8,6 @@ from typing import List
 
 import torch
 import torchaudio
-from torch import nn as nn
 from torch.optim import Adam
 from torch.utils.data import DataLoader
 from torchaudio.datasets.utils import bg_iterator

examples/pipeline_wavernn/wavernn_inference_wrapper.py

@@ -34,6 +34,7 @@ from processing import (
     bits_to_normalized_waveform,
 )
 
+
 class WaveRNNInferenceWrapper(torch.nn.Module):
 
     def __init__(self, wavernn: WaveRNN):