class_docstr: Sampler implementation that converts predictions to GT using registered
  samplers for different fields of `Instances`.
docstr: Remove sampler for a field
  
  Args:
    prediction_attr (str): field to replace with a sampled value
    gt_attr (Optional[str]): field to store the sampled value to, if not None
docstr_tok: ["Remove","sampler","for","a","field","Args",":","prediction_attr","(","str",")",":","field","to","replace","with","a","sampled","value","gt_attr","(","Optional","[","str","]",")",":","field","to","store","the","sampled","value","to",",","if","not","None"]
code: def remove_sampler(
      self,
      prediction_attr: str,
      gt_attr: Optional[str] = None,
  ):
      """
      Remove sampler for a field
  
      Args:
        prediction_attr (str): field to replace with a sampled value
        gt_attr (Optional[str]): field to store the sampled value to, if not None
      """
      assert (prediction_attr, gt_attr) in self._samplers
      del self._samplers[(prediction_attr, gt_attr)]
  
file_path: detectron2/projects/DensePose/densepose/data/samplers/prediction_to_gt.py
imports: from dataclasses import dataclass
  from typing import Any, Callable, Dict, List, Optional
  from detectron2.structures import Instances
label_desc: Use image node_id 4 for calling the PredictionToGroundTruthSampler obj's underlying member method code with example usage: obj.remove_sampler(prediction_attr, gt_attr) without return types