code: def test_simplied_basic_block():
      with pytest.raises(AssertionError):
          # Not implemented yet.
          dcn = dict(
              type="DCN", deform_groups=1, fallback_on_stride=False
          )
          SimplifiedBasicBlock(64, 64, dcn=dcn)
  
      with pytest.raises(AssertionError):
          # Not implemented yet.
          plugins = [
              dict(
                  cfg=dict(type="ContextBlock", ratio=1.0 / 16),
                  position="after_conv3",
              )
          ]
          SimplifiedBasicBlock(64, 64, plugins=plugins)
  
      with pytest.raises(AssertionError):
          # Not implemented yet
          plugins = [
              dict(
                  cfg=dict(
                      type="GeneralizedAttention",
                      spatial_range=-1,
                      num_heads=8,
                      attention_type="0010",
                      kv_stride=2,
                  ),
                  position="after_conv2",
              )
          ]
          SimplifiedBasicBlock(64, 64, plugins=plugins)
  
      with pytest.raises(AssertionError):
          # Not implemented yet
          SimplifiedBasicBlock(64, 64, with_cp=True)
  
      # test SimplifiedBasicBlock structure and forward
      block = SimplifiedBasicBlock(64, 64)
      assert block.conv1.in_channels == 64
      assert block.conv1.out_channels == 64
      assert block.conv1.kernel_size == (3, 3)
      assert block.conv2.in_channels == 64
      assert block.conv2.out_channels == 64
      assert block.conv2.kernel_size == (3, 3)
      x = torch.randn(1, 64, 56, 56)
      x_out = block(x)
      assert x_out.shape == torch.Size([1, 64, 56, 56])
  
      # test SimplifiedBasicBlock without norm
      block = SimplifiedBasicBlock(64, 64, norm_cfg=None)
      assert block.norm1 is None
      assert block.norm2 is None
      x_out = block(x)
      assert x_out.shape == torch.Size([1, 64, 56, 56])
  
file_path: mmdetection/tests/test_models/test_backbones/test_resnet.py
imports: import pytest
  import torch
  from mmcv.ops import DeformConv2dPack
  from torch.nn.modules import AvgPool2d, GroupNorm
  from torch.nn.modules.batchnorm import _BatchNorm
  from mmdet.models.backbones import ResNet, ResNetV1d
  from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
  from mmdet.models.layers import ResLayer, SimplifiedBasicBlock
  from .utils import check_norm_state, is_block, is_norm
label_desc: Use image node_id 4 for calling a global function with example usage: test_simplied_basic_block() without return types