I have the following custom trainer in tensorflow code (1.14).

class CorefModel(object):

  def __init__(self, config):

    #some initialization...
    input_props = []

    input_props.append((tf.int32, [None, None])) # input_ids.
    input_props.append((tf.int32, [None, None])) # input_mask
    input_props.append((tf.int32, [None])) # Text lengths.
    input_props.append((tf.int32, [None, None])) # Speaker IDs.
    input_props.append((tf.int32, [])) # Genre.
    input_props.append((tf.bool, [])) # Is training.
    input_props.append((tf.int32, [None])) # Gold starts.
    input_props.append((tf.int32, [None])) # Gold ends.
    input_props.append((tf.int32, [None])) # Cluster ids.
    input_props.append((tf.int32, [None])) # Sentence Map

    self.queue_input_tensors = [tf.placeholder(dtype, shape) for dtype, shape in input_props]
    dtypes, shapes = zip(*input_props)
    queue = tf.PaddingFIFOQueue(capacity=10, dtypes=dtypes, shapes=shapes)
    self.enqueue_op = queue.enqueue(self.queue_input_tensors)
    self.input_tensors = queue.dequeue()

    self.predictions, self.loss = self.get_predictions_and_loss(*self.input_tensors)

I know that tensorflow 2.* uses tf.dataset, but I do not understand how to use the initial placeholders (all of them with unknown sizes in the beginning) to create the dataset.