class_docstr: Implements Adam algorithm.
  
  Currently GPU-only.
  
  This version of fused Adam implements 2 fusions.
  
    * Fusion of the Adam update's elementwise operations
    * A multi-tensor apply launch that batches the elementwise updates applied to all the model's parameters into one or a few kernel launches.
  
  Adam was been proposed in `Adam: A Method for Stochastic Optimization`_.
  
  Arguments:
      params (iterable): iterable of parameters to optimize or dicts defining
          parameter groups.
      lr (float, optional): learning rate. (default: 1e-3)
      betas (Tuple[float, float], optional): coefficients used for computing
          running averages of gradient and its square. (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)
      amsgrad (boolean, optional): whether to use the AMSGrad variant of this
          algorithm from the paper `On the Convergence of Adam and Beyond`_
          (default: False) NOT SUPPORTED in FusedAdam!
      adam_w_mode (boolean, optional): Apply L2 regularization or weight decay
          True for decoupled weight decay(also known as AdamW) (default: True)
      set_grad_none (bool, optional): whether set grad to None when zero_grad()
          method is called. (default: True)
  
  .. _Adam - A Method for Stochastic Optimization:
      https://arxiv.org/abs/1412.6980
  .. _On the Convergence of Adam and Beyond:
      https://openreview.net/forum?id=ryQu7f-RZ
code: def zero_grad(self):
      if self.set_grad_none:
          for group in self.param_groups:
              for p in group["params"]:
                  p.grad = None
      else:
          super(FusedAdam, self).zero_grad()
  
imports: import torch
  from .multi_tensor_apply import MultiTensorApply
  from ..op_builder import FusedAdamBuilder
label_desc: Use image node_id 2 for calling the FusedAdam obj's underlying member method code with example usage: obj.zero_grad() without return types