original_docstring: 
          Evaluate/summarize the performance, after processing all input/output pairs.
  
          Returns:
              dict:
                  A new evaluator class can return a dict of arbitrary format
                  as long as the user can process the results.
                  In our train_net.py, we expect the following format:
  
                  * key: the name of the task (e.g., bbox)
                  * value: a dict of {metric name: score}, e.g.: {"AP50": 80}
          
docstring: Evaluate/summarize the performance, after processing all input/output pairs.
docstring_tokens: Evaluate / summarize the performance after processing all input / output pairs .
code: def evaluate(self):
          """
          Evaluate/summarize the performance, after processing all input/output pairs.
  
          Returns:
              dict:
                  A new evaluator class can return a dict of arbitrary format
                  as long as the user can process the results.
                  In our train_net.py, we expect the following format:
  
                  * key: the name of the task (e.g., bbox)
                  * value: a dict of {metric name: score}, e.g.: {"AP50": 80}
          """
          pass
code_tokens: def evaluate ( self ) : pass
short_docstring: Evaluate/summarize the performance, after processing all input/output pairs.
short_docstring_tokens: Evaluate / summarize the performance after processing all input / output pairs .
comment: """
          Evaluate/summarize the performance, after processing all input/output pairs.
  
          Returns:
              dict:
                  A new evaluator class can return a dict of arbitrary format
                  as long as the user can process the results.
                  In our train_net.py, we expect the following format:
  
                  * key: the name of the task (e.g., bbox)
                  * value: a dict of {metric name: score}, e.g.: {"AP50": 80}
          """