code: def __init__(
      self,
      input_dim,
      clamp_grad=True,
      max_grad_value=256,
      eps=1e-5,
      elementwise_affine=True,
  ):
      super().__init__()
      self.torch_module = torch.nn.LayerNorm(
          input_dim, eps=eps, elementwise_affine=elementwise_affine
      )
      if clamp_grad:
          hook = partial(
              layer_norm_backward_hook, clamp_value=max_grad_value
          )
          self.torch_module.register_backward_hook(hook)
  
imports: import math
  import re
  from functools import partial
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from torch import Tensor
  from torch import device
  from fairseq.models import FairseqEncoder
  from fairseq.models.speech_to_text.utils import NoOp, attention_suppression, layer_norm_backward_hook, lengths_to_padding_mask, segments_to_sequence
label_desc: Use image node_id 1 to create a new Fp32LayerNorm object from inherited base classes: nn with example: obj = Fp32LayerNorm(input_dim, clamp_grad, max_grad_value, eps, elementwise_affine)