class_docstr: DatasetBuilder initialized from a Croissant config file.
class_docstr_tok: ["DatasetBuilder","initialized","from","a","Croissant","config","file","."]
docstr: Infers the features dict for the required record set.
docstr_tok: ["Infers","the","features","dict","for","the","required","record","set","."]
code: def get_features(self) -> Optional[feature_lib.FeatureConnector]:
      """Infers the features dict for the required record set."""
      record_set = self.get_record_set(self.builder_config.name)
  
      fields = record_set.fields
      features = {}
      for field in fields:
          feature = datatype_converter(
              field,
              int_dtype=self._int_dtype,
              float_dtype=self._float_dtype,
          )
          if field.repeated:
              feature = sequence_feature.Sequence(feature)
          features[field.name] = feature
      return features_dict.FeaturesDict(features)
  
imports: from typing import Any, Dict, Optional, Sequence
  from etils import epath
  import numpy
  from tensorflow_datasets.core import dataset_builder
  from tensorflow_datasets.core import dataset_info
  from tensorflow_datasets.core import download
  from tensorflow_datasets.core import split_builder
  from tensorflow_datasets.core import splits
  from tensorflow_datasets.core.features import feature
  from tensorflow_datasets.core.features import features_dict
  from tensorflow_datasets.core.features import image_feature
  from tensorflow_datasets.core.features import sequence_feature
  from tensorflow_datasets.core.features import text_feature
  from tensorflow_datasets.core.utils import type_utils
  from tensorflow_datasets.core.utils.lazy_imports_utils import mlcroissant
  from tensorflow_datasets.core.utils.lazy_imports_utils import pandas
label_desc: Use image node_id 5 for calling the CroissantBuilder obj's underlying member method code with example usage: obj.get_features() and returns: features_dict