message: Remove the usage of TF private API boosted_trees_ops boosted_trees_bucketize from Keras.
diff: @@ -25,7 +25,7 @@ from tensorflow.python.framework import tensor_spec
  from tensorflow.python.keras.engine import base_preprocessing_layer
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.ops import array_ops
  -from tensorflow.python.ops import boosted_trees_ops
  +from tensorflow.python.ops import gen_boosted_trees_ops
  from tensorflow.python.ops import gen_math_ops
  from tensorflow.python.ops.parallel_for import control_flow_ops
  from tensorflow.python.ops.ragged import ragged_functional_ops
  @@ -92,7 +92,7 @@ class Discretization(base_preprocessing_layer.PreprocessingLayer):
  def call(self, inputs):
  def _bucketize_op(bins):
  bins = [gen_math_ops.cast(bins, dtypes.float32)]
  - return lambda inputs: boosted_trees_ops.boosted_trees_bucketize( # pylint: disable=g-long-lambda
  + return lambda inputs: gen_boosted_trees_ops.BoostedTreesBucketize( # pylint: disable=g-long-lambda
  float_values=[gen_math_ops.cast(inputs, dtypes.float32)],
  bucket_boundaries=bins)[0]
  @@ -105,8 +105,8 @@ class Discretization(base_preprocessing_layer.PreprocessingLayer):
  # this can cause errors in Graph mode, so wrap the tensor in an identity.
  return array_ops.identity(integer_buckets)
  elif isinstance(inputs, sparse_tensor.SparseTensor):
  - integer_buckets = boosted_trees_ops.boosted_trees_bucketize(
  - [gen_math_ops.cast(inputs.values, dtypes.float32)],
  + integer_buckets = gen_boosted_trees_ops.BoostedTreesBucketize(
  + float_values=[gen_math_ops.cast(inputs.values, dtypes.float32)],
  bucket_boundaries=[gen_math_ops.cast(array_ops.squeeze(self.bins),
  dtypes.float32)])[0]
  return sparse_tensor.SparseTensor(
  