class_docstr: Normalized Linear Layer.
  
  Args:
      tempeature (float, optional): Tempeature term. Defaults to 20.
      power (int, optional): Power term. Defaults to 1.0.
      eps (float, optional): The minimal value of divisor to
           keep numerical stability. Defaults to 1e-6.
class_docstr_tok: ["Normalized","Linear","Layer",".","Args",":","tempeature","(","float",",","optional",")",":","Tempeature","term",".","Defaults","to","20.","power","(","int",",","optional",")",":","Power","term",".","Defaults","to","1.0.","eps","(","float",",","optional",")",":","The","minimal","value","of","divisor","to","keep","numerical","stability",".","Defaults","to","1e-6","."]
code: def __init__(
      self,
      *args,
      tempearture: float = 20,
      power: int = 1.0,
      eps: float = 1e-6,
      **kwargs,
  ) -> None:
      super().__init__(*args, **kwargs)
      self.tempearture = tempearture
      self.power = power
      self.eps = eps
      self.init_weights()
  
file_path: mmdetection/mmdet/models/layers/normed_predictor.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmengine.utils import digit_version
  from torch import Tensor
  from mmdet.registry import MODELS
label_desc: Use image node_id 1 to create a new NormedLinear object from inherited base classes: nn with example: obj = NormedLinear()