Instruction:
Can you generate the documentation for the following Python 3 function
def write_data(hyper_params,
               mode,
               sequence,
               num_threads):
    """
    Write a tf record containing a feature dict and a label dict.

    :param hyper_params: The hyper parameters required for writing {"problem": {"augmentation": {"steps": Int}}}
    :param mode: The mode specifies the purpose of the data. Typically it is either "train" or "validation".
    :param sequence: A tf.keras.utils.sequence.
    :param num_threads: The number of threads. (Recommended: 4 for training and 2 for validation seems to works nice)
    :return:
    """
    if not isinstance(sequence, Sequence) and not (callable(getattr(sequence, "__getitem__", None)) and callable(getattr(sequence, "__len__", None))):
        raise ValueError("sequence must be tf.keras.utils.Sequence or a subtype or implement __len__(self) and __getitem__(self, idx)")
    prefix = os.path.join(hyper_params.train.get("tf_records_path", "tfrecords"), mode)
    prefix = prefix.replace("\\", "/")
    data_tmp_folder = "/".join(prefix.split("/")[:-1])
    if not os.path.exists(data_tmp_folder):
        os.makedirs(data_tmp_folder)

    args = [(hyper_params, sequence, num_threads, i, (prefix + "_%d.tfrecords") % i) for i in range(num_threads)]

    # Retrieve a single batch
    sample_feature, sample_label = sequence[0]

    config = {"num_threads": num_threads}
    for k in sample_feature.keys():
        config["feature_" + k] = {"shape": sample_feature[k].shape[1:], "dtype": sample_feature[k].dtype.name}
    for k in sample_label.keys():
        config["label_" + k] = {"shape": sample_label[k].shape[1:], "dtype": sample_label[k].dtype.name}

    with open(prefix + '_config.json', 'w') as outfile:
        json.dump(config, outfile)

    pool = Pool(processes=num_threads)
    pool.map(_write_tf_record_pool_helper, args)

Response: Writes a tf record containing a feature dict and a label dict.


Instruction:
Write a Python 3 function that can
set the context. env and context. env_short and context. account_alias attributes.

Response:
def env(self, value):
    """
    Sets context.env, context.env_short, and context.account_alias if env is valid
    For envs of the form "global.<account>" and "mgmt.<account_alias>",
    env is captured as "global" or "mgmt" and account_alias is parsed
    out of the full env rather than looked up
    Args:
      value: the fully-qualified env value
    Raises:
      ValueError if env is not valid
    """
    env_valid(value)
    self._env_full = value
    if value.find(".") == -1:
      # plain environment, e.g. prod, staging, proto<n>
      self._env = value
      self._account_alias = get_account_alias(value)
    else:
      # "<env>.<account_alias>" form, e.g. global.ellationeng or mgmt.ellationeng
      self._env, self._account_alias = value.split(".")
      # since we extracted an env, must reconfirm that it's legit
      global_env_valid(self._env)
    self._env_short = get_env_short(value)


Instruction:
Explain what the following Python 3 code does
def service_registry(self, sr):
    if type(sr) is not EFServiceRegistry:
      raise TypeError("sr value must be type 'EFServiceRegistry'")
    self._service_registry = sr

Response: Sets the service registry object in context doesn t check it


Instruction:
How would you code a function in Python 3 to
set the current account id for the next commit

Response:
def account_id(self, value):
    """
    Sets the current account id

    Args:
      value: current account id (string)

    Returns:
      None
    """
    if type(value) is not str:
      raise TypeError("commit value must be string")
    self._account_id = value