code: def _get_loss(self, loss):
      if callable(loss):
          return loss
      elif isinstance(loss, str):
          assert loss in ["NT_Xent"]
          return {"NT_Xent": NTXent_loss}[loss]
      else:
          raise NotImplementedError(
              "The argument `loss` should be str or callable which returns a loss tensor"
          )
  
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 2 for calling the BaseContrastiveTrainer obj's underlying member method code with example usage: obj._get_loss(loss) and returns: loss, dict