code: def __init__(self, num_samples, num_features):
      super(JittorRandomMaxDataset, self).__init__()
      self.x = jt.randn((num_samples, num_features))
      self.y = self.x.argmax(dim=1)[0]
  
imports: import pytest
  from fastNLP.core.callbacks import callback
  from fastNLP.core.controllers.trainer import Trainer
  from fastNLP.core.controllers.trainer import Evaluator
  from fastNLP.core.metrics.accuracy import Accuracy
  from fastNLP.core.callbacks.progress_callback import RichCallback
  from fastNLP.core.dataloaders.jittor_dataloader.fdl import JittorDataLoader
  from fastNLP.envs.imports import _NEED_IMPORT_JITTOR
label_desc: Use image node_id 1 to create a new JittorRandomMaxDataset object from inherited base classes: Dataset with example: obj = JittorRandomMaxDataset(num_samples, num_features)