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 __init__(
      self,
      transforms: List[Callable[[np.ndarray], np.ndarray]],
      loss: Callable[[np.ndarray], float],
  ) -> None:
      self.loss = loss
      self.transforms = transforms
      self._scalar = 1.0
  
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 1 to create a new FunctionChunk object  with example: obj = FunctionChunk(transforms, loss)