class_docstr: Applies a `Batch Normalization <https://arxiv.org/abs/1502.03167>`_ over a 4D input tensor in FP32
class_docstr_tok: ["Applies","a","`","Batch","Normalization","<","https",":","\/\/arxiv.org\/abs\/1502.03167",">","`","_","over","a","4D","input","tensor","in","FP32"]
code: def __init__(
      self,
      num_features: int,
      eps: Optional[float] = 1e-5,
      momentum: Optional[float] = 0.1,
      affine: Optional[bool] = True,
      track_running_stats: Optional[bool] = True,
      *args,
      **kwargs,
  ) -> None:
      super().__init__(
          num_features=num_features,
          eps=eps,
          momentum=momentum,
          affine=affine,
          track_running_stats=track_running_stats,
          *args,
          **kwargs,
      )
  
file_path: ml-cvnets/cvnets/layers/normalization/batch_norm.py
imports: from typing import Optional
  import torch
  from torch import Tensor, nn
  from cvnets.layers.normalization import register_norm_fn
label_desc: Use image node_id 1 to create a new BatchNorm2dFP32 object from inherited base classes: BatchNorm2d with example: obj = BatchNorm2dFP32(num_features, eps, momentum, affine, track_running_stats)