class_docstr: Implements Lamb algorithm.
  
  It has been proposed in `Large Batch Optimization for Deep Learning:
  Training BERT in 76 minutes`__.
  
  Arguments:
      params: iterable of parameters to optimize or dicts defining
          parameter groups
      lr: learning rate (default: 1e-3)
      betas: coefficients used for computing
          running averages of gradient and its square (default: (0.9, 0.999))
      eps: term added to the denominator to improve
          numerical stability (default: 1e-8)
      weight_decay: weight decay (L2 penalty) (default: 0)
      clamp_value: clamp weight_norm in (0,clamp_value) (default: 10)
          set to a high value to avoid it (e.g 10e3)
      adam: always use trust ratio = 1, which turns this
          into Adam. Useful for comparison purposes. (default: False)
      debias: debias adam by (1 - beta**step) (default: False)
  
  Example:
      >>> import torch_optimizer as optim
      >>> optimizer = optim.Lamb(model.parameters(), lr=0.1)
      >>> optimizer.zero_grad()
      >>> loss_fn(model(input), target).backward()
      >>> optimizer.step()
  
  __ https://arxiv.org/abs/1904.00962
  
  Note:
      Reference code: https://github.com/cybertronai/pytorch-lamb
docstr: Performs a single optimization step.
  
  Arguments:
      closure: A closure that reevaluates the model and returns the loss.
docstr_tok: ["Performs","a","single","optimization","step",".","Arguments",":","closure",":","A","closure","that","reevaluates","the","model","and","returns","the","loss","."]
code: def step(self, closure: OptLossClosure = None) -> OptFloat:
      r"""Performs a single optimization step.
  
      Arguments:
          closure: 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:
                  msg = (
                      "Lamb does not support sparse gradients, "
                      "please consider SparseAdam instead"
                  )
                  raise RuntimeError(msg)
  
              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, memory_format=torch.preserve_format
                  )
                  # Exponential moving average of squared gradient values
                  state["exp_avg_sq"] = torch.zeros_like(
                      p, memory_format=torch.preserve_format
                  )
  
              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
              # m_t
              exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
              # v_t
              exp_avg_sq.mul_(beta2).addcmul_(
                  grad, grad, value=1 - beta2
              )
  
              # Paper v3 does not use debiasing.
              if self.debias:
                  bias_correction = math.sqrt(
                      1 - beta2 ** state["step"]
                  )
                  bias_correction /= 1 - beta1 ** state["step"]
              else:
                  bias_correction = 1
  
              # Apply bias to lr to avoid broadcast.
              step_size = group["lr"] * bias_correction
  
              weight_norm = torch.norm(p.data).clamp(
                  0, self.clamp_value
              )
  
              adam_step = exp_avg / exp_avg_sq.sqrt().add(group["eps"])
              if group["weight_decay"] != 0:
                  adam_step.add_(p.data, alpha=group["weight_decay"])
  
              adam_norm = torch.norm(adam_step)
              if weight_norm == 0 or adam_norm == 0:
                  trust_ratio = 1
              else:
                  trust_ratio = weight_norm / adam_norm
              state["weight_norm"] = weight_norm
              state["adam_norm"] = adam_norm
              state["trust_ratio"] = trust_ratio
              if self.adam:
                  trust_ratio = 1
  
              p.data.add_(adam_step, alpha=-step_size * trust_ratio)
  
      return loss
  
imports: import math
  import torch
  from torch.optim.optimizer import Optimizer
  from .types import Betas2, OptFloat, OptLossClosure, Params
label_desc: Use image node_id 2 for calling the Lamb obj's underlying member method code with example usage: obj.step(closure) and returns: loss