class_docstr: Crops or pads all label and meta-data features to have uniform shapes.
class_docstr_tok: ["Crops","or","pads","all","label","and","meta-data","features","to","have","uniform","shapes","."]
code: def __call__(self, features: Features) -> Features:
      image_scalar_sequences = [
          self.negative_labels_key,
          self.negative_text_labels_key,
          self.not_exhaustive_labels_key,
      ]
      instance_scalar_sequences = [
          self.instance_text_labels_key,
          self.area_key,
          self.crowds_key,
          self.annotation_id_key,
          self.instance_labels_key,
          self.instance_multi_labels_key,
      ]
      for key in image_scalar_sequences:
          if key in features:
              features[key] = transforms.crop_or_pad_sequence(
                  features[key], self.image_multilabels, self.allow_crop
              )
      for key in instance_scalar_sequences:
          if key in features:
              features[key] = transforms.crop_or_pad_sequence(
                  features[key], self.num_instances, self.allow_crop
              )
      return 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 CropOrPadMetaData obj's underlying member method code with example usage: obj.__call__(features) and returns: features