code: def __init__(
      self, in_channel, out_channel, kernel_size=3, stride=1, groups=1
  ):
      padding = (kernel_size - 1) // 2
      super(ConvBNReLU, self).__init__(
          nn.Conv2d(
              in_channel,
              out_channel,
              kernel_size,
              stride,
              padding,
              groups=groups,
              bias=False,
          ),
          nn.BatchNorm2d(out_channel),
          nn.ReLU6(inplace=True),
      )
  
file_path: deep-learning-for-image-processing/deploying_service/deploying_pytorch/pytorch_flask_service/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)