code: def _testGradientSliceTape(
      self, input_shape, slice_begin, slice_size
  ):
      with backprop.GradientTape() as tape:
          num_inputs = np.prod(input_shape)
          num_grads = np.prod(slice_size)
          inp = (
              np.random.rand(num_inputs)
              .astype("f")
              .reshape(input_shape)
          )
          a = constant_op.constant(
              [float(x) for x in inp.ravel(order="C")],
              shape=input_shape,
              dtype=dtypes.float32,
          )
          tape.watch(a)
          slice_t = array_ops.slice(a, slice_begin, slice_size)
          grads = (
              np.random.rand(num_grads).astype("f").reshape(slice_size)
          )
          grad_tensor = constant_op.constant(grads)
      grad = tape.gradient(slice_t, [a], grad_tensor)[0]
      result = self.evaluate(grad)
  
      # Create a zero tensor of the input shape ane place
      # the grads into the right location to compare against TensorFlow.
      np_ans = np.zeros(input_shape)
      slices = []
      for i in range(len(input_shape)):
          slices.append(
              slice(slice_begin[i], slice_begin[i] + slice_size[i])
          )
      np_ans[tuple(slices)] = grads
  
      self.assertAllClose(np_ans, result)
  
imports: import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 18 for calling the SliceTest obj's underlying member method code with example usage: obj._testGradientSliceTape(input_shape, slice_begin, slice_size) without return types