How to slice according to batch in the tensorflow array？

If you want to use Tensorflow only, try combining tf.gather with tf.range and tf.ragged.stack:

import tensorflow as tf

output = tf.constant([
                      [[1, 2, 3]], 
                      [[4, 5, 6]], 
                      [[7, 8, 9]]
                      ])

subject_ids = tf.constant([[0, 1], [1, 2], [0, 2]])

ragged_ouput = tf.ragged.stack([tf.gather(output, tf.range(subject_ids[i, 0], subject_ids[i, 1] + 1)) for i in tf.range(0, tf.shape(subject_ids)[0])], axis=0)
ragged_ouput = tf.squeeze(ragged_ouput, axis=2)
print(ragged_ouput)

[[[1, 2, 3], [4, 5, 6]], [[4, 5, 6], [7, 8, 9]], [[1, 2, 3], [4, 5, 6], [7, 8, 9]]]

Update 1:

import tensorflow as tf
tf.config.run_functions_eagerly(True)

output = tf.constant([
                      [[1, 2, 3]], 
                      [[4, 5, 6]], 
                      [[7, 8, 9]]
                      ])

subject_ids = tf.constant([[0, 1], [1, 2], [0, 2]])

def slice_tensor(x):
  return tf.ragged.stack([tf.gather(output, tf.range(x[0], x[1] + 1))], axis=0)

ragged_ouput = tf.map_fn(slice_tensor, subject_ids, fn_output_signature=tf.RaggedTensorSpec(shape=[1, None, None, 3],
                                                                    dtype=tf.int32,
                                                                    ragged_rank=2,
                                                                    row_splits_dtype=tf.int64))
ragged_ouput = tf.squeeze(ragged_ouput, axis=1)
tf.print(ragged_ouput, summarize=-1)

[[[[1, 2, 3]], [[4, 5, 6]]], [[[4, 5, 6]], [[7, 8, 9]]], [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]]]]