code: def __init__(self, feature_dimension=10, data_num=1000, seed=0):
      self.num_labels = feature_dimension
      self.feature_dimension = feature_dimension
      self.data_num = data_num
      self.seed = seed
  
      g = torch.Generator()
      g.manual_seed(1000)
      self.x = torch.randint(
          low=-100,
          high=100,
          size=[data_num, feature_dimension],
          generator=g,
      ).float()
      self.y = torch.max(self.x, dim=-1)[1]
  
imports: from functools import reduce
  from numpy import dtype
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
label_desc: Use image node_id 1 to create a new TorchArgMaxDataset object from inherited base classes: Dataset with example: obj = TorchArgMaxDataset(feature_dimension, data_num, seed)