class_docstr: Identifies and tracks unique objects within a list, even if they are
  duplicates based on internal memory addresses (using id()).
  It Optimizes operations involving unique objects by avoiding redundant
  processing.
  
  Examples
  --------
  >>> from deepchem.utils import Uniquifier
  >>> a = 1
  >>> b = 2
  >>> c = 3
  >>> d = 1
  >>> u = Uniquifier([a, b, c, a, d])
  >>> u.get_unique_objs()
  [1, 2, 3]
docstr: Map the unique objects to the original objects.
  
  Parameters
  ----------
  uniqueobjs: List
      The list of unique objects.
docstr_tok: ["Map","the","unique","objects","to","the","original","objects",".","Parameters","--","--","--","--","--","uniqueobjs",":","List","The","list","of","unique","objects","."]
code: def map_unique_objs(self, uniqueobjs: List) -> List:
      """Map the unique objects to the original objects.
  
      Parameters
      ----------
      uniqueobjs: List
          The list of unique objects.
  
      """
      assert len(uniqueobjs) == self.num_unique, (
          "The uniqueobjs must have %d elements" % self.num_unique
      )
      if self.all_unique:
          return uniqueobjs
      return [uniqueobjs[idx] for idx in self.nonunique_map_idxs]
  
file_path: deepchem/deepchem/utils/misc_utils.py
imports: from typing import Dict, List, Optional
label_desc: Use image node_id 3 for calling the Uniquifier obj's underlying member method code with example usage: obj.map_unique_objs(uniqueobjs) and returns: unknown, uniqueobjs