code: def __init__(
      self,
      in_chs,
      out_chs,
      kernel_size,
      stride,
      padding=0,
      groups=1,
      activation_fn=nn.ReLU(inplace=True),
  ):
      super(BnActConv2d, self).__init__()
      self.bn = nn.BatchNorm2d(in_chs, eps=0.001)
      self.act = activation_fn
      self.conv = nn.Conv2d(
          in_chs,
          out_chs,
          kernel_size,
          stride,
          padding,
          groups=groups,
          bias=False,
      )
  
imports: import os
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.model_zoo
  from collections import OrderedDict
  from .adaptive_avgmax_pool import adaptive_avgmax_pool2d
  from .convert_from_mxnet import convert_from_mxnet, has_mxnet
label_desc: Use image node_id 1 to create a new BnActConv2d object from inherited base classes: nn with example: obj = BnActConv2d(in_chs, out_chs, kernel_size, stride, padding, groups, activation_fn)