code: def test_sequence_unpack_with_broadcast_as(device_id, precision):
      x = C.sequence.input_variable(5)
      a = C.sequence.input_variable(4, sequence_axis=C.Axis("a"))
      y, mask = C.sequence.unpack(x, 0).outputs
      bvm = C.sequence.broadcast_as(0 * C.reduce_sum(y) + mask, a)
  
      x1 = [
          np.arange(7 * 5).reshape(7, 5).astype("f"),
          np.arange(3 * 5).reshape(3, 5).astype("f"),
      ]
      a1 = [
          np.arange(3 * 4).reshape(3, 4).astype("f"),
          np.arange(6 * 4).reshape(6, 4).astype("f"),
      ]
  
      expected = [
          np.ones((3, 7), dtype=np.float32),
          np.ones((6, 7), dtype=np.float32),
      ]
      expected[1][:, 3:] = 0
  
      actual = bvm.eval({x: x1, a: a1})
      for actual_i, expected_i in zip(actual, expected):
          assert np.allclose(actual_i, expected_i)
  
imports: from __future__ import division
  import numpy
  import pytest
  import cntk
  from .ops_test_utils import cntk_device
  from cntk.tests.test_utils import _to_dense, _to_csr
  from .ops_test_utils import unittest_helper, _test_unary_op, _test_binary_op, AA, precision, PRECISION_TO_TYPE, cntk_device
  from cntk import Value
  from cntk.axis import Axis
  from cntk.internal import sanitize_dtype_cntk
  from ..None import constant
label_desc: Use image node_id 27 for calling a global function with example usage: test_sequence_unpack_with_broadcast_as(device_id, precision) without return types