code: def __init__(
      self, in_channels, out_channels, kernel_size=3, stride=1
  ):
      super(BlazeBlock, self).__init__()
  
      self.stride = stride
      self.channel_pad = out_channels - in_channels
  
      # TFLite uses slightly different padding than PyTorch
      # on the depthwise conv layer when the stride is 2.
      if stride == 2:
          self.max_pool = nn.MaxPool2d(
              kernel_size=stride, stride=stride
          )
          padding = 0
      else:
          padding = (kernel_size - 1) // 2
  
      self.convs = nn.Sequential(
          nn.Conv2d(
              in_channels=in_channels,
              out_channels=in_channels,
              kernel_size=kernel_size,
              stride=stride,
              padding=padding,
              groups=in_channels,
              bias=True,
          ),
          nn.Conv2d(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=1,
              stride=1,
              padding=0,
              bias=True,
          ),
      )
  
      self.act = nn.ReLU(inplace=True)
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new BlazeBlock object from inherited base classes: nn with example: obj = BlazeBlock(in_channels, out_channels, kernel_size, stride)