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 _apply_transforms(self, x: np.ndarray) -> np.ndarray:
      if not self.transforms:
          return x
      y = self.transforms[0](x)
      for transf in self.transforms[1:]:
          y = transf(y)
      return y
  
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 2 for calling the FunctionChunk obj's underlying member method code with example usage: obj._apply_transforms(x) and returns: y, x