docstr: The function of predicting on the given dataset.
  
  Parameters
  ----------
  dataset: The graph classification dataset used to be predicted.
  
  mask: `str`
  "train", "val" or "test"
  The dataset mask.
  
  Returns
  -------
  The prediction result of `predict_proba`.
docstr_tok: ["The","function","of","predicting","on","the","given","dataset",".","Parameters","--","--","--","--","--","dataset",":","The","graph","classification","dataset","used","to","be","predicted",".","mask",":","`","str","`","''","train","''",",","``","val","''","or","``","test","''","The","dataset","mask",".","Returns","--","--","--","-","The","prediction","result","of","`","predict_proba","`","."]
code: def predict(self, dataset, mask="test"):
      """
      The function of predicting on the given dataset.
  
      Parameters
      ----------
      dataset: The graph classification dataset used to be predicted.
  
      mask: `str`
      "train", "val" or "test"
      The dataset mask.
  
      Returns
      -------
      The prediction result of `predict_proba`.
      """
      if self.graph_level:
          loader = utils.graph_get_split(
              dataset,
              mask,
              batch_size=self.batch_size,
              num_workers=self.num_workers,
          )
      else:
          # TODO node level
          loader = None
      return self._predict_proba(loader, in_log_format=True).max(1)[1]
  
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 27 for calling the BaseContrastiveTrainer obj's underlying member method code with example usage: obj.predict(dataset, mask) and returns: self