code: def _vpsde_beta_t(
      t: int, T: int, min_beta: float, max_beta: float
  ) -> float:
      """Beta Scheduler.
  
      Args:
          t (int): current step.
          T (int): total steps.
          min_beta (float): minimum beta.
          max_beta (float): maximum beta.
  
      Returns:
          float: current beta.
  
      """
      t_coef = (2 * t - 1) / (T**2)
      return 1.0 - np.exp(
          -min_beta / T - 0.5 * (max_beta - min_beta) * t_coef
      )
  
imports: import math
  from typing import Optional, Union
  import numpy
  import torch
  from torch import nn
  from torch.nn import functional
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
label_desc: Use image node_id 1 for calling a global function with example usage: _vpsde_beta_t(t, T, min_beta, max_beta) and returns: unknown