code: def __init__(
      self,
      params,
      lr=1e-2,
      etas=(0.5, 1.2),
      step_sizes=(1e-6, 50),
      *,
      foreach: Optional[bool] = None,
      maximize: bool = False,
      differentiable: bool = False,
  ):
      if not 0.0 <= lr:
          raise ValueError(f"Invalid learning rate: {lr}")
      if not 0.0 < etas[0] < 1.0 < etas[1]:
          raise ValueError(f"Invalid eta values: {etas[0]}, {etas[1]}")
  
      defaults = dict(
          lr=lr,
          etas=etas,
          step_sizes=step_sizes,
          foreach=foreach,
          maximize=maximize,
          differentiable=differentiable,
      )
      super().__init__(params, defaults)
  
file_path: pytorch/torch/optim/rprop.py
imports: import torch
  from torch import Tensor
  from .optimizer import Optimizer, _use_grad_for_differentiable, _default_to_fused_or_foreach, _differentiable_doc, _foreach_doc, _maximize_doc, _view_as_real
  from typing import List, Optional
label_desc: Use image node_id 1 to create a new Rprop object from inherited base classes: Optimizer with example: obj = Rprop(params, lr, etas, step_sizes)