code: def __init__(
      self,
      inplanes,
      planes,
      stride=1,
      downsample=None,
      groups=1,
      base_width=64,
      dilation=1,
      eps=1e-5,
      dropblock=0.0,
  ):
      super(IBasicBlock, self).__init__()
      if groups != 1 or base_width != 64:
          raise ValueError(
              "BasicBlock only supports groups=1 and base_width=64"
          )
      if dilation > 1:
          raise NotImplementedError(
              "Dilation > 1 not supported in BasicBlock"
          )
      self.bn1 = nn.BatchNorm2d(inplanes, eps=eps)
      self.conv1 = conv3x3(inplanes, planes)
      self.bn2 = nn.BatchNorm2d(planes, eps=eps)
      self.prelu = nn.PReLU(planes)
      self.conv2 = conv3x3(planes, planes, stride)
      self.bn3 = nn.BatchNorm2d(planes, eps=eps)
      self.downsample = downsample
      self.stride = stride
      self.dbs = None
      if dropblock > 0.0:
          import timm
          from timm.layers import DropBlock2d
  
          self.dbs = [
              DropBlock2d(dropblock, 7),
              DropBlock2d(dropblock, 7),
              DropBlock2d(dropblock, 7),
          ]
  
imports: import torch
  from torch import nn
  import torch.nn.functional
  import logging
label_desc: Use image node_id 1 to create a new IBasicBlock object from inherited base classes: nn with example: obj = IBasicBlock(inplanes, planes, stride, downsample, groups, base_width, dilation, eps, dropblock)