code: def __init__(
      self,
      num_labels,
      feature_dimension=2,
      each_label_data=1000,
      seed=0,
  ):
      self.num_labels = num_labels
      self.feature_dimension = feature_dimension
      self.each_label_data = each_label_data
      self.seed = seed
  
      torch.manual_seed(seed)
      self.x_center = torch.randint(
          low=-100, high=100, size=[num_labels, feature_dimension]
      )
      random_shuffle = (
          torch.randn([num_labels, each_label_data, feature_dimension])
          / 10
      )
      self.x = (
          self.x_center.unsqueeze(1).expand(
              num_labels, each_label_data, feature_dimension
          )
          + random_shuffle
      )
      self.x = self.x.view(
          num_labels * each_label_data, feature_dimension
      )
      self.y = reduce(
          lambda x, y: x + y,
          [[i] * each_label_data for i in range(num_labels)],
      )
  
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 TorchNormalDataset_Classification object from inherited base classes: Dataset with example: obj = TorchNormalDataset_Classification(num_labels, feature_dimension, each_label_data, seed)