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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def build_tfexample_transfored_training_input_fn(schema, features, analysis_output_dir, raw_data_file_pattern, training_batch_size, num_epochs=None, randomize_input=False, min_after_dequeue=1, reader_num_threads=1, allow_smaller_final_batch=True):
 """Creates training input_fn that reads transformed tf.example files. Args: schema: schema list features: features dict analysis_output_dir: output folder from analysis raw_data_file_pattern: file path, or list of files training_batch_size: An int specifying the batch size to use. num_epochs: numer of epochs to read from the files. Use None to read forever. randomize_input: If true, the input rows are read out of order. This randomness is limited by the min_after_dequeue value. min_after_dequeue: Minimum number elements in the reading queue after a dequeue, used to ensure a level of mixing of elements. Only used if randomize_input is True. reader_num_threads: The number of threads enqueuing data. allow_smaller_final_batch: If false, fractional batches at the end of training or evaluation are not used. Returns: An input_fn suitable for training that reads transformed data in tf record files of tf.example. """


  def transformed_training_input_fn():
    """Training input function that reads transformed data."""

    if isinstance(raw_data_file_pattern, six.string_types):
      filepath_list = [raw_data_file_pattern]
    else:
      filepath_list = raw_data_file_pattern

    files = []
    for path in filepath_list:
      files.extend(file_io.get_matching_files(path))

    filename_queue = tf.train.string_input_producer(
        files, num_epochs=num_epochs, shuffle=randomize_input)

    options = tf.python_io.TFRecordOptions(
        compression_type=tf.python_io.TFRecordCompressionType.GZIP)
    ex_id, ex_str = tf.TFRecordReader(options=options).read_up_to(
        filename_queue, training_batch_size)

    queue_capacity = (reader_num_threads + 3) * training_batch_size + min_after_dequeue
    if randomize_input:
      _, batch_ex_str = tf.train.shuffle_batch(
          tensors=[ex_id, ex_str],
          batch_size=training_batch_size,
          capacity=queue_capacity,
          min_after_dequeue=min_after_dequeue,
          enqueue_many=True,
          num_threads=reader_num_threads,
          allow_smaller_final_batch=allow_smaller_final_batch)

    else:
      _, batch_ex_str = tf.train.batch(
          tensors=[ex_id, ex_str],
          batch_size=training_batch_size,
          capacity=queue_capacity,
          enqueue_many=True,
          num_threads=reader_num_threads,
          allow_smaller_final_batch=allow_smaller_final_batch)

    feature_spec = {}
    feature_info = get_transformed_feature_info(features, schema)
    for name, info in six.iteritems(feature_info):
      if info['size'] is None:
        feature_spec[name] = tf.VarLenFeature(dtype=info['dtype'])
      else:
        feature_spec[name] = tf.FixedLenFeature(shape=[info['size']], dtype=info['dtype'])

    parsed_tensors = tf.parse_example(batch_ex_str, feature_spec)

    # Expand the dims of non-sparse tensors. This is needed by tf.learn.
    transformed_features = {}
    for k, v in six.iteritems(parsed_tensors):
      if isinstance(v, tf.Tensor) and v.get_shape().ndims == 1:
        transformed_features[k] = tf.expand_dims(v, -1)
      else:
        # Sparse tensor
        transformed_features[k] = v

    transformed_features = image_feature_engineering(
        features=features,
        feature_tensors_dict=transformed_features)

    # Remove the target tensor, and return it directly
    target_name = get_target_name(features)
    if not target_name or target_name not in transformed_features:
      raise ValueError('Cannot find target transform in features')

    transformed_target = transformed_features.pop(target_name)

    return transformed_features, transformed_target

  return transformed_training_input_fn