message: Use tf.nn.l2_normalize instead of dividing by norm explicitly.
diff: @@ -23,6 +23,7 @@ from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import math_ops
  +from tensorflow.python.ops import nn
  from tensorflow.python.ops.linalg import linalg_impl as linalg
  from tensorflow.python.ops.linalg import linear_operator
  from tensorflow.python.ops.linalg import linear_operator_util
  @@ -209,8 +210,7 @@ class LinearOperatorHouseholder(linear_operator.LinearOperator):
  reflection_axis = ops.convert_to_tensor_v2_with_dispatch(
  self.reflection_axis)
  x = linalg.adjoint(x) if adjoint_arg else x
  - normalized_axis = reflection_axis / linalg.norm(
  - reflection_axis, axis=-1, keepdims=True)
  + normalized_axis = nn.l2_normalize(reflection_axis, axis=-1)
  mat = normalized_axis[..., array_ops.newaxis]
  x_dot_normalized_v = math_ops.matmul(mat, x, adjoint_a=True)
  @@ -240,8 +240,7 @@ class LinearOperatorHouseholder(linear_operator.LinearOperator):
  def _to_dense(self):
  reflection_axis = ops.convert_to_tensor_v2_with_dispatch(
  self.reflection_axis)
  - normalized_axis = reflection_axis / linalg.norm(
  - reflection_axis, axis=-1, keepdims=True)
  + normalized_axis = nn.l2_normalize(reflection_axis, axis=-1)
  mat = normalized_axis[..., array_ops.newaxis]
  matrix = -2 * math_ops.matmul(mat, mat, adjoint_b=True)
  return array_ops.matrix_set_diag(
  @@ -250,8 +249,7 @@ class LinearOperatorHouseholder(linear_operator.LinearOperator):
  def _diag_part(self):
  reflection_axis = ops.convert_to_tensor_v2_with_dispatch(
  self.reflection_axis)
  - normalized_axis = reflection_axis / linalg.norm(
  - reflection_axis, axis=-1, keepdims=True)
  + normalized_axis = nn.l2_normalize(reflection_axis, axis=-1)
  return 1. - 2 * normalized_axis * math_ops.conj(normalized_axis)
  def _eigvals(self):
  