code: def _check_valid_opt_params(lr, eps, betas):
      if lr < 0:
          raise ValueError(f"Invalid learning rate: {lr}")
      if eps < 0:
          raise ValueError(f"Invalid epsilon value: {eps}")
      if not (0.0 <= betas[0] < 1.0 and 0.0 <= betas[1] < 1.0):
          raise ValueError(f"Betas have to be between 0 and 1: {betas}")
  
file_path: NeMo/nemo/core/optim/novograd.py
imports: import torch
  from torch.optim.optimizer import Optimizer
label_desc: Use image node_id 1 for calling a global function with example usage: _check_valid_opt_params(lr, eps, betas) without return types