class_docstr: 只实现一个 forward 函数，来测试用户自己在外面初始化 DDP 的场景；
class_docstr_tok: ["\u53ea\u5b9e\u73b0\u4e00\u4e2a","forward","\u51fd\u6570\uff0c\u6765\u6d4b\u8bd5\u7528\u6237\u81ea\u5df1\u5728\u5916\u9762\u521d\u59cb\u5316","DDP","\u7684\u573a\u666f\uff1b"]
code: def __init__(self, num_labels, feature_dimension):
      super(TorchNormalModel_Classification_2, 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_2 object from inherited base classes: Module with example: obj = TorchNormalModel_Classification_2(num_labels, feature_dimension)