class_docstr: Linear layer with deterministic reset_parameters for testing.
class_docstr_tok: ["Linear","layer","with","deterministic","reset_parameters","for","testing","."]
code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
  
imports: import itertools
  import sys
  from typing import Union
  import torch
  import torch.distributed
  import torch.nn
  from torch.distributed.fsdp import FullyShardedDataParallel, MixedPrecision
  from torch.distributed.fsdp.wrap import always_wrap_policy, enable_wrap, ModuleWrapPolicy, wrap
  from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
  from torch.testing._internal.common_fsdp import FSDPTest
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, run_tests, skip_but_pass_in_sandcastle_if, TEST_WITH_DEV_DBG_ASAN
label_desc: Use image node_id 1 to create a new MyLinear object from inherited base classes: nn with example: obj = MyLinear()