class_docstr: 基础的 Jittor 分类模型
class_docstr_tok: ["\u57fa\u7840\u7684","Jittor","\u5206\u7c7b\u6a21\u578b"]
code: def __init__(self, num_labels, feature_dimension):
      super(JittorNormalModel_Classification, self).__init__()
      self.num_labels = num_labels
  
      self.linear1 = nn.Linear(
          in_features=feature_dimension, out_features=64
      )
      self.ac1 = nn.ReLU()
      self.linear2 = nn.Linear(in_features=64, out_features=32)
      self.ac2 = nn.ReLU()
      self.output = nn.Linear(in_features=32, out_features=num_labels)
      self.loss_fn = nn.CrossEntropyLoss()
  
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 JittorNormalModel_Classification object from inherited base classes: Module with example: obj = JittorNormalModel_Classification(num_labels, feature_dimension)