class_docstr: Applies `Layer Normalization <https://arxiv.org/abs/1607.06450>`_ over a input tensor with FP32 precision
code: def forward(self, x: Tensor) -> Tensor:
      # Convert input from dtype X to FP32 and perform normalization operation.
      # This may help with underflow/overflow issues that we typically see with normalization layers
      inp_dtype = x.dtype
      return super().forward(x.to(torch.float32)).to(inp_dtype)
  
file_path: ml-cvnets/cvnets/layers/normalization/layer_norm.py
imports: from typing import List, Optional, Union
  import torch
  from torch import Size, Tensor, nn
  from cvnets.layers.normalization import register_norm_fn
label_desc: Use image node_id 2 for calling the LayerNormFP32 obj's underlying member method code with example usage: obj.forward(x) and returns: super