code: def step(self, closure=None):
      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.float()
              if grad.is_sparse:
                  raise RuntimeError(
                      "RAdam does not support sparse gradients"
                  )
  
              p_data_fp32 = p.data.float()
  
              state = self.state[p]
  
              if len(state) == 0:
                  state["step"] = 0
                  state["exp_avg"] = torch.zeros_like(p_data_fp32)
                  state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
              else:
                  state["exp_avg"] = state["exp_avg"].type_as(
                      p_data_fp32
                  )
                  state["exp_avg_sq"] = state["exp_avg_sq"].type_as(
                      p_data_fp32
                  )
  
              exp_avg, exp_avg_sq = (
                  state["exp_avg"],
                  state["exp_avg_sq"],
              )
              beta1, beta2 = group["betas"]
  
              exp_avg_sq.mul_(beta2).addcmul_(
                  grad, grad, value=1 - beta2
              )
              exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
  
              state["step"] += 1
              buffered = group["buffer"][int(state["step"] % 10)]
              if state["step"] == buffered[0]:
                  N_sma, step_size = buffered[1], buffered[2]
              else:
                  buffered[0] = state["step"]
                  beta2_t = beta2 ** state["step"]
                  N_sma_max = 2 / (1 - beta2) - 1
                  N_sma = N_sma_max - 2 * state["step"] * beta2_t / (
                      1 - beta2_t
                  )
                  buffered[1] = N_sma
  
                  # more conservative since it's an approximated value
                  if N_sma >= 5:
                      step_size = math.sqrt(
                          (1 - beta2_t)
                          * (N_sma - 4)
                          / (N_sma_max - 4)
                          * (N_sma - 2)
                          / N_sma
                          * N_sma_max
                          / (N_sma_max - 2)
                      ) / (1 - beta1 ** state["step"])
                  elif self.degenerated_to_sgd:
                      step_size = 1.0 / (1 - beta1 ** state["step"])
                  else:
                      step_size = -1
                  buffered[2] = step_size
  
              # more conservative since it's an approximated value
              if N_sma >= 5:
                  if group["weight_decay"] != 0:
                      p_data_fp32.add_(
                          p_data_fp32,
                          alpha=-group["weight_decay"] * group["lr"],
                      )
                  denom = exp_avg_sq.sqrt().add_(group["eps"])
                  p_data_fp32.addcdiv_(
                      exp_avg, denom, value=-step_size * group["lr"]
                  )
                  p.data.copy_(p_data_fp32)
              elif step_size > 0:
                  if group["weight_decay"] != 0:
                      p_data_fp32.add_(
                          p_data_fp32,
                          alpha=-group["weight_decay"] * group["lr"],
                      )
                  p_data_fp32.add_(
                      exp_avg, alpha=-step_size * group["lr"]
                  )
                  p.data.copy_(p_data_fp32)
  
      return loss
  
file_path: TTS/TTS/utils/radam.py
imports: import math
  import torch
  from torch.optim.optimizer import Optimizer
label_desc: Use image node_id 3 for calling the RAdam obj's underlying member method code with example usage: obj.step(closure) and returns: loss