class_docstr: Given an LVIS TFDS example, creates features with boxes.
  
  The processing in this class includes:
  1. Converting images from uint8 to float32 with range [0, 1]. Note that TFDS
     already parses the serialized protos and decodes jpeg images into uint8.
  2. Renaming keys to modality names.
class_docstr_tok: ["Given","an","LVIS","TFDS","example",",","creates","features","with","boxes",".","The","processing","in","this","class","includes",":","1",".","Converting","images","from","uint8","to","float32","with","range","[","0",",","1","]",".","Note","that","TFDS","already","parses","the","serialized","protos","and","decodes","jpeg","images","into","uint8",".","2",".","Renaming","keys","to","modality","names","."]
code: def __call__(self, features: Features) -> Features:
      new_features = super().__call__(features)
      new_features[self.negative_labels_key] = tf.cast(
          features["neg_category_ids"], tf.int32
      )
      # A non-standard feature representing category ids not fully covered
      # by instance labels.
      new_features[self.not_exhaustive_labels_key] = tf.cast(
          features["not_exhaustive_category_ids"], tf.int32
      )
      return new_features
  
imports: import abc
  import dataclasses
  from typing import Optional, Sequence, Tuple, Union
  from clu import preprocess_spec
  from scenic.projects.owl_vit.preprocessing import modalities
  from scenic.projects.owl_vit.preprocessing import transforms
  import tensorflow
label_desc: Use image node_id 1 for calling the DecodeLvisExample obj's underlying member method code with example usage: obj.__call__(features) and returns: new_features