class_docstr: Quantized version of `torch.nn.LayerNorm`. Adds quantization-specific arguments on top of `torch.nn.LayerNorm`.
  
  Args:
      output_bit (`int`, *optional*, defaults to `8`):
          Bitwidth for the layer output activation.
      quant_mode (`bool`, *optional*, defaults to `False`):
          Whether or not the layer is quantized.
      force_dequant (`str`, *optional*, defaults to `"none"`):
          Force dequantize the layer if either "layernorm" or "nonlinear" is given.
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docstr: This fallback function is called when overflow is detected during training time, and adjusts the `self.shift`
  to avoid overflow in the subsequent runs.
docstr_tok: ["This","fallback","function","is","called","when","overflow","is","detected","during","training","time",",","and","adjusts","the","`","self.shift","`","to","avoid","overflow","in","the","subsequent","runs","."]
code: def overflow_fallback(self, y_int):
      """
      This fallback function is called when overflow is detected during training time, and adjusts the `self.shift`
      to avoid overflow in the subsequent runs.
      """
      self.set_shift(y_int)  # adjusts `self.shift`
      y_int_shifted = floor_ste.apply(y_int / 2**self.shift)
      y_sq_int = y_int_shifted**2
      var_int = torch.sum(y_sq_int, axis=2, keepdim=True)
      return var_int
  
imports: import decimal
  import numpy
  import torch
  from torch import nn
  from torch.autograd import Function
  from ...utils import logging
label_desc: Use image node_id 3 for calling the IntLayerNorm obj's underlying member method code with example usage: obj.overflow_fallback(y_int) and returns: var_int