class_docstr: Most tests done in the base class LinearOperatorDerivedClassTest.
code: def operator_and_matrix(
      self,
      build_info,
      dtype,
      use_placeholder,
      ensure_self_adjoint_and_pd=False,
  ):
      shape = list(build_info.shape)
      reflection_axis = linear_operator_test_util.random_sign_uniform(
          shape[:-1], minval=1.0, maxval=2.0, dtype=dtype
      )
      # Make sure unit norm.
      reflection_axis = reflection_axis / linalg_ops.norm(
          reflection_axis, axis=-1, keepdims=True
      )
  
      lin_op_reflection_axis = reflection_axis
  
      if use_placeholder:
          lin_op_reflection_axis = array_ops.placeholder_with_default(
              reflection_axis, shape=None
          )
  
      operator = householder.LinearOperatorHouseholder(
          lin_op_reflection_axis
      )
  
      mat = reflection_axis[..., array_ops.newaxis]
      matrix = -2 * math_ops.matmul(mat, mat, adjoint_b=True)
      matrix = array_ops.matrix_set_diag(
          matrix, 1.0 + array_ops.matrix_diag_part(matrix)
      )
  
      return operator, matrix
  
imports: from tensorflow.python.framework import config
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import linalg_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.linalg import linalg
  from tensorflow.python.ops.linalg import linear_operator_householder
  from tensorflow.python.ops.linalg import linear_operator_test_util
  from tensorflow.python.platform import test
label_desc: Use image node_id 5 for calling the LinearOperatorHouseholderTest obj's underlying member method code with example usage: obj.operator_and_matrix(build_info, dtype, use_placeholder, ensure__adjoint_and_pd) and returns: operator,  matrix