code: def __init__(
      self,
      tokenizer_name,
      num_sample_negative=85,
      max_tokens=256,
      full_sampling_prob=0.5,
      label_map_file=None,
  ):
      if AutoTokenizer is None:
          raise RuntimeError(
              "transformers is not installed, please install it by: "
              "pip install transformers."
          )
  
      self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
      self.num_sample_negative = num_sample_negative
      self.full_sampling_prob = full_sampling_prob
      self.max_tokens = max_tokens
      self.label_map = None
      if label_map_file:
          with open(label_map_file, "r") as file:
              self.label_map = json.load(file)
  
file_path: mmdetection/mmdet/datasets/transforms/text_transformers.py
imports: import json
  from mmcv.transforms import BaseTransform
  from mmdet.registry import TRANSFORMS
  from mmdet.structures.bbox import BaseBoxes
  import random
  import re
  import numpy
label_desc: Use image node_id 1 to create a new RandomSamplingNegPos object from inherited base classes: BaseTransform with example: obj = RandomSamplingNegPos(tokenizer_name, num_sample_negative, max_tokens, full_sampling_prob, label_map_file)