code: def __init__(
      self, in_planes, out_planes, kernel_size=3, stride=1, groups=1
  ):
      self.channel_pad = out_planes - in_planes
      self.stride = stride
      # padding = (kernel_size - 1) // 2
  
      # TFLite uses slightly different padding than PyTorch
      if stride == 2:
          padding = 0
      else:
          padding = (kernel_size - 1) // 2
  
      super(ConvBNReLU, self).__init__(
          nn.Conv2d(
              in_planes,
              out_planes,
              kernel_size,
              stride,
              padding,
              groups=groups,
              bias=False,
          ),
          nn.BatchNorm2d(out_planes),
          nn.ReLU6(inplace=True),
      )
      self.max_pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
  
imports: import os
  import sys
  import torch
  import torch.nn
  import torch.utils.model_zoo
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new ConvBNReLU object from inherited base classes: nn with example: obj = ConvBNReLU(in_planes, out_planes, kernel_size, stride, groups)