class_docstr: `FeatureConnector` for translations with fixed languages per example.
  
  Input: The Translate feature accepts a dictionary for each example mapping
    string language codes to string translations.
  
  Output: A dictionary mapping string language codes to translations as `Text`
    features.
  
  Example:
  At construction time:
  
  ```
  tfds.features.Translation(languages=['en', 'fr', 'de'])
  ```
  
  During data generation:
  
  ```
  yield {
      'en': 'the cat',
      'fr': 'le chat',
      'de': 'die katze'
  }
  ```
  
  Tensor returned by `.as_dataset()`:
  
  ```
  {
      'en': tf.Tensor(shape=(), dtype=np.str_, numpy='the cat'),
      'fr': tf.Tensor(shape=(), dtype=np.str_, numpy='le chat'),
      'de': tf.Tensor(shape=(), dtype=np.str_, numpy='die katze'),
  }
  ```
code: def to_json_content(
      self,
  ) -> (
      feature_pb2.TranslationFeature
  ):  # pytype: disable=signature-mismatch  # overriding-return-type-checks
      if self._encoder or self._encoder_config:
          raise ValueError(
              "TFDS encoder are deprecated and will be removed soon. "
              "Please use `tensorflow_text` instead with the plain text dataset."
          )
      return feature_pb2.TranslationFeature(
          languages=self.languages, variable_languages_per_example=False
      )
  
imports: from __future__ import annotations
  import collections.abc
  from typing import Union
  from tensorflow_datasets.core.features import feature
  from tensorflow_datasets.core.features import features_dict
  from tensorflow_datasets.core.features import sequence_feature
  from tensorflow_datasets.core.features import text_feature
  from tensorflow_datasets.core.proto import feature_pb2
  from tensorflow_datasets.core.utils import type_utils
label_desc: Use image node_id 4 for calling the Translation obj's underlying member method code with example usage: obj.to_json_content() and returns: feature_pb2