class_docstr: 单独实现 train_step 和 evaluate_step；
docstr: 如果不加参数 y，那么应该在 trainer 中设置 output_mapping = {"y": "target"}；
docstr_tok: ["\u5982\u679c\u4e0d\u52a0\u53c2\u6570","y\uff0c\u90a3\u4e48\u5e94\u8be5\u5728","trainer","\u4e2d\u8bbe\u7f6e","output_mapping","=","{","``","y","''",":","``","target","''","}","\uff1b"]
code: def evaluate_step(self, x, y):
      """
      如果不加参数 y，那么应该在 trainer 中设置 output_mapping = {"y": "target"}；
      """
  
      x = self(x)
      x = oneflow.max(x, dim=-1)[1]
      return {"pred": x, "target": y}
  
imports: from fastNLP.envs.imports import _NEED_IMPORT_ONEFLOW
label_desc: Use image node_id 4 for calling the OneflowNormalModel_Classification_1 obj's underlying member method code with example usage: obj.evaluate_step(x, y) and returns: dict