class_docstr: Computes a sequence of transforms and applies a loss on it.
  This can be left multiplied by a scalar to add a weight.
class_docstr_tok: ["Computes","a","sequence","of","transforms","and","applies","a","loss","on","it",".","This","can","be","left","multiplied","by","a","scalar","to","add","a","weight","."]
code: def __repr__(self) -> str:
      return f"{self._scalar}*FunctionChunck({self.transforms}, {self.loss}"
  
imports: from typing import List, Callable, Dict, Type, Optional
  from pathlib import Path
  import re
  import numpy
  from nevergrad.common.decorators import Registry
  from nevergrad.functions import ExperimentFunction
  import nevergrad
  from .None import _core
label_desc: Use image node_id 5 for calling the FunctionChunk obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str, self