code: def views_fn(self, views_fn):
      self.views_fn_opt = views_fn
      # set augmentation methods
      if isinstance(views_fn, list):
          if isinstance(self.aug_ratio, float):
              self.aug_ratio = [self.aug_ratio] * len(views_fn)
          if isinstance(self.aug_ratio, list) and len(
              self.aug_ratio
          ) != len(views_fn):
              self.aug_ratio = [self.aug_ratio[0]] * len(views_fn)
      self._views_fn = self._get_views_fn(views_fn, self.aug_ratio)
  
imports: import os
  import torch
  import torch_geometric
  import logging
  import numpy
  import typing
  from tqdm import trange
  from typing import Union, Tuple, Sequence, Type, Callable
  import torch.nn.functional
  import torch.utils.data
  from torch.optim.lr_scheduler import StepLR, MultiStepLR, ExponentialLR, ReduceLROnPlateau
  from .losses import NTXent_loss
  from .utils import get_view_by_name
  from autogl.module.model.encoders.base_encoder import AutoHomogeneousEncoderMaintainer
  from ..base import BaseTrainer, EarlyStopping, _DummyModel
  from ..evaluation import Evaluation, get_feval, Acc
  from ...model import BaseAutoModel, BaseEncoderMaintainer, BaseDecoderMaintainer, EncoderUniversalRegistry, DecoderUniversalRegistry
  from ....utils import get_logger
  from ....datasets import utils
label_desc: Use image node_id 14 for calling the BaseContrastiveTrainer obj's underlying member method code with example usage: obj.views_fn(views_fn) without return types