code: def test_eye_like_no_backprop(input_data, sparse_output, precision):
      if sparse_output:
          # as gradients now do not support sparse, so return to skip the test
          return
      from .. import eye_like
  
      dt = PRECISION_TO_TYPE[precision]
      data = AA(input_data, dtype=dt)
  
      x = C.input_variable(
          shape=data.shape[1:],
          dtype=sanitize_dtype_cntk(dt),
          needs_gradient=True,
          name="a",
      )
      w = C.parameter(x.shape, init=np.ones(x.shape).astype(dt) * 3.0)
      expected_x_backward = np.zeros_like(data)
      expected_w_backward = np.zeros_like(w)
      op = lambda operand: eye_like(
          operand, sparse_output=sparse_output
      )
      _no_backprop_test(
          x, op, data, w, expected_x_backward, expected_w_backward
      )
      _no_backprop_sgd_test(op, x, w, dt, data, one_step_sgd)
  
file_path: CNTK/bindings/python/cntk/ops/tests/stop_gradient_test.py
imports: import numpy
  import cntk
  from .ops_test_utils import AA, PRECISION_TO_TYPE, precision
  from cntk.internal import sanitize_dtype_cntk
  import pytest
  from .non_diff_test import Matrices
label_desc: Use image node_id 9 for calling a global function with example usage: test_eye_like_no_backprop(input_data, sparse_output, precision) without return types