class_docstr: Linear layer with deterministic reset_parameters for testing.
class_docstr_tok: ["Linear","layer","with","deterministic","reset_parameters","for","testing","."]
code: def reset_parameters(self, *args, **kwargs):
      torch.manual_seed(42)
      with torch.no_grad():
          # Use an initialization method that depends on shape
          torch.nn.init.xavier_uniform_(self.weight, 1.0)
  
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 2 for calling the MyLinear obj's underlying member method code with example usage: obj.reset_parameters() without return types