code: def __init__(
      self,
      verbose=False,
      max_iter=100,
      tol=1e-2,
      stability=0,
      gas_boundary_resolution=1,
      layer_name="",
      layer_num=0,
  ):
      super().__init__()
  
      self.verbose = verbose
      self.max_iter = max_iter
      self.tol = tol
      self.stability = stability
      self.gas_boundary_resolution = gas_boundary_resolution
      self.layer_name = layer_name
      self.layer_num = layer_num
  
      assert len(self.layer_name) > 0 and layer_num > 0
  
      log_dist(
          f"enabled eigenvalue with verbose={verbose}, max_iter={max_iter}, tol={tol}, stability={stability}, gas_boundary_resolution={gas_boundary_resolution}, layer_name={layer_name}, layer_num={layer_num}",
          ranks=[0],
      )
  
file_path: DeepSpeed/deepspeed/runtime/eigenvalue.py
imports: import torch
  from deepspeed.utils import log_dist
  import numpy
  import logging
label_desc: Use image node_id 1 to create a new Eigenvalue object from inherited base classes: object with example: obj = Eigenvalue(verbose, max_iter, tol, stability, gas_boundary_resolution, layer_name, layer_num)