code: def __init__(
      self, in_planes, planes, stride=1, groups=1, base_width=64
  ):
      assert (
          groups == 1 and base_width == 64
      ), "BasicBlock only supports groups=1 and base_width=64"
      self.conv1 = nn.Conv2d(
          in_planes,
          planes,
          kernel_size=3,
          stride=stride,
          padding=1,
          bias=False,
      )
      self.bn1 = nn.BatchNorm2d(planes)
      self.conv2 = nn.Conv2d(
          planes, planes, kernel_size=3, padding=1, stride=1, bias=False
      )
      self.bn2 = nn.BatchNorm2d(planes)
      self.downsample = []
      if stride != 1 or in_planes != self.expansion * planes:
          self.downsample = [
              nn.Conv2d(
                  in_planes,
                  self.expansion * planes,
                  kernel_size=1,
                  stride=stride,
                  bias=False,
              ),
              nn.BatchNorm2d(self.expansion * planes),
          ]
  
file_path: tinygrad/extra/models/resnet.py
imports: import tinygrad.nn
  from tinygrad.tensor import Tensor
  from tinygrad.nn.state import torch_load
  from tinygrad.helpers import fetch, get_child
label_desc: Use image node_id 1 to create a new BasicBlock object  with example: obj = BasicBlock(in_planes, planes, stride, groups, base_width)