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","."]
docstr: Forward function for `NormedLinear`.
docstr_tok: ["Forward","function","for","`","NormedLinear","`","."]
code: def forward(self, x: Tensor) -> Tensor:
      """Forward function for `NormedLinear`."""
      weight_ = self.weight / (
          self.weight.norm(dim=1, keepdim=True).pow(self.power)
          + self.eps
      )
      x_ = x / (x.norm(dim=1, keepdim=True).pow(self.power) + self.eps)
      x_ = x_ * self.tempearture
  
      return F.linear(x_, weight_, self.bias)
  
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 3 for calling the NormedLinear obj's underlying member method code with example usage: obj.forward(x) and returns: F