class_docstr: Fused BatchNorm to use in Conformer to improve accuracy in finetuning with TTS scenario
  Drop-in replacement for BatchNorm1d with simple affine projection
class_docstr_tok: ["Fused","BatchNorm","to","use","in","Conformer","to","improve","accuracy","in","finetuning","with","TTS","scenario","Drop-in","replacement","for","BatchNorm1d","with","simple","affine","projection"]
docstr: Args:
      num_features: number of channels, see original BatchNorm1d documentation
docstr_tok: ["Args",":","num_features",":","number","of","channels",",","see","original","BatchNorm1d","documentation"]
code: def __init__(self, num_features: int):
      """
      Args:
          num_features: number of channels, see original BatchNorm1d documentation
      """
      super().__init__()
      self.num_features = num_features
      self.weight = nn.Parameter(torch.ones(num_features))
      self.bias = nn.Parameter(torch.zeros(num_features))
  
file_path: NeMo/nemo/collections/asr/parts/submodules/batchnorm.py
imports: from __future__ import annotations
  from functools import reduce
  from typing import List
  import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new FusedBatchNorm1d object from inherited base classes: nn with example: obj = FusedBatchNorm1d(num_features)