code: def __init__(
      self,
      in_channel,
      out_channel,
      kernel_size=3,
      stride=1,
      groups=1,
      norm_layer=None,
  ):
      padding = (kernel_size - 1) // 2
      if norm_layer is None:
          norm_layer = nn.BatchNorm2d
      super(ConvBNReLU, self).__init__(
          nn.Conv2d(
              in_channel,
              out_channel,
              kernel_size,
              stride,
              padding,
              groups=groups,
              bias=False,
          ),
          norm_layer(out_channel),
          nn.ReLU6(inplace=True),
      )
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/train_coco_dataset/backbone/mobilenetv2_model.py
imports: from torch import nn
  import torch
label_desc: Use image node_id 1 to create a new ConvBNReLU object from inherited base classes: nn with example: obj = ConvBNReLU(in_channel, out_channel, kernel_size, stride, groups, norm_layer)