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 forward(self, x, y):
      x = self.ac1(self.linear1(x))
      x = self.ac2(self.linear2(x))
      x = self.output(x)
      loss = self.loss_fn(x, y)
      x = torch.max(x, dim=-1)[1]
      return {"loss": loss, "preds": x, "target": y}
  
file_path: fastNLP/tests/helpers/models/torch_model.py
imports: from fastNLP.envs.imports import _NEED_IMPORT_TORCH
label_desc: Use image node_id 2 for calling the TorchNormalModel_Classification_2 obj's underlying member method code with example usage: obj.forward(x, y) and returns: dict