class_docstr: 单独实现 train_step 和 evaluate_step；
class_docstr_tok: ["\u5355\u72ec\u5b9e\u73b0","train_step","\u548c","evaluate_step\uff1b"]
code: def __init__(self, num_labels, feature_dimension):
      super(TorchNormalModel_Classification_1, self).__init__()
      self.num_labels = num_labels
  
      self.linear1 = nn.Linear(
          in_features=feature_dimension, out_features=10
      )
      self.ac1 = nn.ReLU()
      self.linear2 = nn.Linear(in_features=10, out_features=10)
      self.ac2 = nn.ReLU()
      self.output = nn.Linear(in_features=10, out_features=num_labels)
      self.loss_fn = nn.CrossEntropyLoss()
  
file_path: fastNLP/tests/helpers/models/torch_model.py
imports: from fastNLP.envs.imports import _NEED_IMPORT_TORCH
label_desc: Use image node_id 1 to create a new TorchNormalModel_Classification_1 object from inherited base classes: Module with example: obj = TorchNormalModel_Classification_1(num_labels, feature_dimension)