class_docstr: An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
  models.
class_docstr_tok: ["An","abstract","class","to","handle","weights","initialization","and","a","simple","interface","for","downloading","and","loading","pretrained","models","."]
code: def normal_(self, data: torch.Tensor):
      # with FSDP, module params will be on CUDA, so we cast them back to CPU
      # so that the RNG is consistent with and without FSDP
      data.copy_(data.cpu().normal_(mean=0.0, std=0.02).to(data.device))
  
imports: import math
  from typing import Iterable, Iterator, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithNoAttention, SequenceClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_graphormer import GraphormerConfig
label_desc: Use image node_id 1 for calling the GraphormerPreTrainedModel obj's underlying member method code with example usage: obj.normal_(data) without return types