code: def __init__(self, block, layers, num_classes=1000):
      self.inplanes = 64
      super(DRN_A, self).__init__()
      self.out_dim = 512 * block.expansion
      self.conv1 = nn.Conv2d(
          3, 64, kernel_size=7, stride=2, padding=3, bias=False
      )
      self.bn1 = nn.BatchNorm2d(64)
      self.relu = nn.ReLU(inplace=True)
      self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
      self.layer1 = self._make_layer(block, 64, layers[0])
      self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
      self.layer3 = self._make_layer(
          block, 256, layers[2], stride=1, dilation=2
      )
      self.layer4 = self._make_layer(
          block, 512, layers[3], stride=1, dilation=4
      )
      self.avgpool = nn.AvgPool2d(28, stride=1)
      self.fc = nn.Linear(512 * block.expansion, num_classes)
  
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
              m.weight.data.normal_(0, math.sqrt(2.0 / n))
          elif isinstance(m, BatchNorm):
              m.weight.data.fill_(1)
              m.bias.data.zero_()
  
imports: import numpy
  import torch
  from torch.autograd import Variable
  from pytorch2keras.converter import pytorch_to_keras
  import torch.nn
  import math
label_desc: Use image node_id 1 to create a new DRN_A object from inherited base classes: nn with example: obj = DRN_A(block, layers, num_classes)