class_docstr: An instance ``scaler`` helps perform the steps of gradient scaling
  conveniently.
class_docstr_tok: ["An","instance","``","scaler","``","helps","perform","the","steps","of","gradient","scaling","conveniently","."]
docstr: Multiplies ('scales') a tensor or list of tensors by the scale factor.
  
  Returns scaled outputs.
  
  Args:
      outputs (Tensor or iterable of Tensors):  Outputs to scale.
  
  Returns:
      Tensor or iterable of Tensors: Scaled outputs.
docstr_tok: ["Multiplies","(","'scales","'",")","a","tensor","or","list","of","tensors","by","the","scale","factor",".","Returns","scaled","outputs",".","Args",":","outputs","(","Tensor","or","iterable","of","Tensors",")",":","Outputs","to","scale",".","Returns",":","Tensor","or","iterable","of","Tensors",":","Scaled","outputs","."]
code: def scale(self, outputs):  # type: ignore
      """
      Multiplies ('scales') a tensor or list of tensors by the scale factor.
  
      Returns scaled outputs.
  
      Args:
          outputs (Tensor or iterable of Tensors):  Outputs to scale.
  
      Returns:
          Tensor or iterable of Tensors: Scaled outputs.
      """
      return self.loss_scale * outputs
  
file_path: fairscale/fairscale/experimental/optim/dynamic_loss_scaler.py
imports: from collections import defaultdict
  from enum import Enum
  from typing import Dict, List, Optional
  import torch
label_desc: Use image node_id 2 for calling the DynamicLossScaler obj's underlying member method code with example usage: obj.scale(outputs) and returns: unknown