code: def __init__(self, hidden_dim):
      super().__init__()
      # TODO (Min): for both fast and memory efficient conv kernels, we should be using
      #     AMP/fp16 + channel_last input format. Otherwise, cudnn internally does conversion
      #     to channel_last when it is fp16 weights. Leave this knowledge here and perhaps
      #     future test can cover it.
      self.stem = nn.Sequential(
          nn.Conv2d(3, 64, kernel_size=3),
          nn.BatchNorm2d(64),
          nn.ReLU(inplace=True),
      )
      self.blocks = nn.Sequential(
          nn.Conv2d(64, hidden_dim, kernel_size=5, padding=2),
          nn.BatchNorm2d(hidden_dim),
          nn.ReLU(inplace=True),
          nn.Conv2d(hidden_dim, hidden_dim, kernel_size=5, padding=2),
          nn.BatchNorm2d(hidden_dim),
          nn.ReLU(inplace=True),
          nn.Conv2d(hidden_dim, hidden_dim, kernel_size=5, padding=2),
          nn.BatchNorm2d(hidden_dim),
          nn.ReLU(inplace=True),
          nn.AdaptiveAvgPool2d(output_size=(1, 1)),
          nn.Flatten(),
      )
      self.head = nn.Linear(hidden_dim, 10)
  
imports: import contextlib
  import pytest
  import torch
  import torch.multiprocessing
  import torch.nn
  from torch.nn.parallel import DistributedDataParallel
  import torch.optim
  from fairscale.fair_dev.testing.testing import dist_init, dump_all_tensors, skip_if_single_gpu, teardown, temp_files_ctx
  from fairscale.internal import torch_version
  from fairscale.internal.parallel import get_process_group_cached
  from fairscale.nn import checkpoint_wrapper
  from fairscale.nn.data_parallel import FullyShardedDataParallel
  from fairscale.nn.data_parallel import auto_wrap_bn
label_desc: Use image node_id 1 to create a new Model object from inherited base classes: nn with example: obj = Model(hidden_dim)