class_docstr: SSL model with trunk and head.
class_docstr_tok: ["SSL","model","with","trunk","and","head","."]
code: def __init__(self, conv_bias, linear_bias):
      super().__init__()
      print(
          f"relu inplace: {_relu_inplace}, conv bias: {conv_bias}, linear bias: {linear_bias}"
      )
  
      self.trunk = Sequential()
      self.trunk.need_fsdp_wrap = True  # Set a flag for later wrapping.
      stem = Sequential(
          Conv2d(2, 4, (3, 3), (2, 2), (1, 1), bias=conv_bias),
          BatchNorm2d(4),
          ReLU(_relu_inplace),
      )
      any_stage_block1_0 = ResBlock(4, 8)
      self.trunk.add_module("stem", stem)
      self.trunk.add_module(
          "any_stage_block1", Sequential(any_stage_block1_0)
      )
  
      self.head = Sequential(
          Sequential(
              Linear(16, 16, bias=linear_bias),
              ReLU(),
              Linear(16, 8, bias=linear_bias),
          ),  # projection_head
          Linear(8, 15, bias=False),  # prototypes0
      )
  
imports: import contextlib
  from itertools import product
  import random
  import tempfile
  import pytest
  import torch
  from torch.cuda.amp import GradScaler
  import torch.multiprocessing
  from torch.nn import AdaptiveAvgPool2d, BatchNorm2d, Conv2d, CrossEntropyLoss, Linear, Module, ReLU, Sequential, Sigmoid, SyncBatchNorm
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim import SGD
  from fairscale.fair_dev.testing.testing import dist_init, objects_are_equal, rmf, skip_if_single_gpu, state_dict_norm, teardown, torch_cuda_version
  from fairscale.internal import torch_version
  from fairscale.nn.data_parallel import FullyShardedDataParallel
  from fairscale.nn.data_parallel import TrainingState, auto_wrap_bn
label_desc: Use image node_id 1 to create a new Model object from inherited base classes: Module with example: obj = Model(conv_bias, linear_bias)