class_docstr: Implements the AdaHessian algorithm from "ADAHESSIAN: An Adaptive Second OrderOptimizer for Machine Learning"
  
  Arguments:
      params (iterable): iterable of parameters to optimize or dicts defining parameter groups
      lr (float, optional): learning rate (default: 0.1)
      betas ((float, float), optional): coefficients used for computing running averages of gradient and the
          squared hessian trace (default: (0.9, 0.999))
      eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8)
      weight_decay (float, optional): weight decay (L2 penalty) (default: 0.0)
      hessian_power (float, optional): exponent of the hessian trace (default: 1.0)
      update_each (int, optional): compute the hessian trace approximation only after *this* number of steps
          (to save time) (default: 1)
      n_samples (int, optional): how many times to sample `z` for the approximation of the hessian trace (default: 1)
code: def is_second_order(self):
      return True
  
file_path: pytorch-image-models/timm/optim/adahessian.py
imports: import torch
label_desc: Use image node_id 2 for calling the Adahessian obj's underlying member method code with example usage: obj.is_second_order() and returns: True