class_docstr: Definition of a single benchmark to run.
  
  This class is an implementation detail and needs to be a simple datastructure that is easy to
  (de-)serialise.
class_docstr_tok: ["Definition","of","a","single","benchmark","to","run",".","This","class","is","an","implementation","detail","and","needs","to","be","a","simple","datastructure","that","is","easy","to","(","de-",")","serialise","."]
code: def name(self) -> str:
      tokens = []
  
      def print_bool(name: str) -> None:
          value_str = "T" if getattr(self, name) else "F"
          tokens.append(f"{name}={value_str}")
  
      tokens.append(self.dataset_name)
      tokens.append(self.model_name)
      print_bool("do_compile")
      print_bool("do_optimise")
      print_bool("do_predict")
      print_bool("do_posterior")
  
      return "/".join(tokens)
  
imports: from dataclasses import dataclass
  from typing import Any, Collection, Dict, List, Optional, Sequence, Set, Tuple
  import pandas
  from benchmark.dataset_api import DatasetFactory
  from benchmark.grouping import GroupingKey, GroupingSpec
  from benchmark.model_api import ModelFactory
  from benchmark.plotter_api import Plotter
  from benchmark.registry import Named, Registry
label_desc: Use image node_id 1 for calling the BenchmarkTask obj's underlying member method code with example usage: obj.name() and returns: str