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Complete this python program:
def build_input_fn(mode, hparams, data_file_patterns=None, num_datashards=None, fixed_problem=None, worker_replicas=None, worker_id=None):
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#Here is the Python program you requested.

    """Provides input to the graph, either from disk or via a placeholder.
    This function produces an input function that will feed data into
    the network. There are two modes of operation:
    1. If data_file_pattern and all subsequent arguments are None, then
    it creates a placeholder for a serialized tf.Example proto.
    2. If data_file_pattern is defined, it will read the data from the
    files at the given location. Use this mode for training,
    evaluation, and testing prediction.
    Args:
    mode: The execution mode, as defined in tf.contrib.learn.ModeKeys.
    hparams: HParams object.
    data_file_patterns: The list of file patterns to use to read in data. Set to
    `None` if you want to create a placeholder for the input data. The
    `problems` flag is a list of problem names joined by the `-` character.
    The flag\s string is then split along the `-` and each problem gets its
    own example queue.
    num_datashards: An integer.
    fixed_problem: An integer indicating the problem to fetch data for, or None
    if the input is to be randomly selected.
    worker_replicas: int, number of worker replicas. Used in multiproblem
    setting with hparams.problem_choice == distributed.
    worker_id: int, id of this worker replica. Used in multiproblem setting with
    hparams.problem_choice == distributed.
    Returns:
    A function that returns a dictionary of features and the target labels.
    """
    def input_fn():
        "Supplies    input    to    our    model.\n\n                This    function    supplies    input    to    our    model,    where    this    input    is    a\n                function    of    the    mode.    For    example,    we    supply    different    data    if\n                we're    performing    training    versus    evaluation.\n\n                Returns:\n                        A    tuple    consisting    of    1)    a    dictionary    of    tensors    whose    keys    are\n                        the    feature    names,    and    2)    a    tensor    of    target    labels    if    the    mode\n                        is    not    INFER    (and    None,    otherwise).\n\n                Raises:\n                        ValueError:    if    one    of    the    parameters    has    an    unsupported    value.\n                "
        (problem_count, batches) = (len(hparams.problems), [])
        with tf.name_scope('input_reader'):
            for n in xrange(problem_count):
                if ((fixed_problem is not None) and (n != fixed_problem)):
                    continue
                problem_instance = hparams.problem_instances[n]
                p_hparams = hparams.problems[n]
                with tf.name_scope(('problem_%d' % n)):
                    with tf.device('/cpu:0'):
                        capacity = p_hparams.max_expected_batch_size_per_shard
                        capacity *= num_datashards
                        examples = data_reader.input_pipeline(problem_instance, (data_file_patterns and data_file_patterns[n]), capacity, mode, hparams)
                        feature_map = data_reader.batch_examples(examples, data_reader.hparams_to_batching_scheme(hparams, shard_multiplier=num_datashards, drop_long_sequences=((mode == tf.contrib.learn.ModeKeys.TRAIN) or hparams.eval_drop_long_sequences), length_multiplier=p_hparams.batch_size_multiplier))
                if (problem_instance is not None):
                   ...