class_docstr: Implements Adam algorithm with weight decay fix as introduced in `Decoupled Weight Decay Regularization
  <https://arxiv.org/abs/1711.05101>`.
  Parameters:
      params (Iterable[nn.parameter.Parameter]):
          Iterable of parameters to optimize or dictionaries defining parameter groups.
      lr (float, optional):
          The learning rate to use.
      betas (Tuple[float,float], optional):
          Adam's betas parameters (b1, b2).
      eps (float, optional):
          Adam's epsilon for numerical stability.
      weight_decay (float, optional):
          Decoupled weight decay to apply.
      correct_bias (bool, optional):
          Whether or not to correct bias in Adam.
docstr: Performs a single optimization step.
  Arguments:
      closure(Callable, optional):
          A closure that reevaluates the model and returns the loss.
code: def step(self, closure: Callable = None):
      """
      Performs a single optimization step.
      Arguments:
          closure(Callable, optional):
              A closure that reevaluates the model and returns the loss.
      """
      loss = None
      if closure is not None:
          loss = closure()
  
      for group in self.param_groups:
          for p in group["params"]:
              if p.grad is None:
                  continue
              grad = p.grad.data
              if grad.is_sparse:
                  raise RuntimeError(
                      "Adam does not support sparse gradients, please consider SparseAdam instead"
                  )
  
              state = self.state[p]
  
              # State initialization
              if len(state) == 0:
                  state["step"] = 0
                  # Exponential moving average of gradient values
                  state["exp_avg"] = torch.zeros_like(p.data)
                  # Exponential moving average of squared gradient values
                  state["exp_avg_sq"] = torch.zeros_like(p.data)
  
              exp_avg, exp_avg_sq = (
                  state["exp_avg"],
                  state["exp_avg_sq"],
              )
              beta1, beta2 = group["betas"]
  
              state["step"] += 1
  
              # Decay the first and second moment running average coefficient
              # In-place operations to update the averages at the same time
              exp_avg.mul_(beta1).add_(grad, alpha=(1.0 - beta1))
              exp_avg_sq.mul_(beta2).addcmul_(
                  grad, grad, value=1.0 - beta2
              )
              denom = exp_avg_sq.sqrt().add_(group["eps"])
  
              step_size = group["lr"]
              if group["correct_bias"]:  # No bias correction for Bert
                  bias_correction1 = 1.0 - beta1 ** state["step"]
                  bias_correction2 = 1.0 - beta2 ** state["step"]
                  step_size = (
                      step_size
                      * math.sqrt(bias_correction2)
                      / bias_correction1
                  )
  
              p.data.addcdiv_(exp_avg, denom, value=-step_size)
  
              # Just adding the square of the weights to the loss function is *not*
              # the correct way of using L2 regularization/weight decay with Adam,
              # since that will interact with the m and v parameters in strange ways.
              #
              # Instead we want to decay the weights in a manner that doesn't interact
              # with the m/v parameters. This is equivalent to adding the square
              # of the weights to the loss with plain (non-momentum) SGD.
              # Add weight decay at the end (fixed version)
              if group["weight_decay"] > 0.0:
                  p.data.add_(
                      p.data,
                      alpha=(-group["lr"] * group["weight_decay"]),
                  )
  
      return loss
  
file_path: towhee/towhee/trainer/optimization/adamw.py
imports: import math
  from typing import Callable, Iterable, Tuple
  import torch
  from torch import nn
  from torch.optim import Optimizer
label_desc: Use image node_id 2 for calling the AdamW obj's underlying member method code with example usage: obj.step(closure) and returns: loss