code: def build_operator_and_matrix(
      self,
      build_info,
      dtype,
      use_placeholder,
      ensure_self_adjoint_and_pd=False,
      diagonals_format="sequence",
  ):
      shape = list(build_info.shape)
  
      # Ensure that diagonal has large enough values. If we generate a
      # self adjoint PD matrix, then the diagonal will be dominant guaranteeing
      # positive definitess.
      diag = linear_operator_test_util.random_sign_uniform(
          shape[:-1], minval=4.0, maxval=6.0, dtype=dtype
      )
      # We'll truncate these depending on the format
      subdiag = linear_operator_test_util.random_sign_uniform(
          shape[:-1], minval=1.0, maxval=2.0, dtype=dtype
      )
      if ensure_self_adjoint_and_pd:
          # Abs on complex64 will result in a float32, so we cast back up.
          diag = math_ops.cast(math_ops.abs(diag), dtype=dtype)
          # The first element of subdiag is ignored. We'll add a dummy element
          # to superdiag to pad it.
          superdiag = math_ops.conj(subdiag)
          superdiag = manip_ops.roll(superdiag, shift=-1, axis=-1)
      else:
          superdiag = linear_operator_test_util.random_sign_uniform(
              shape[:-1], minval=1.0, maxval=2.0, dtype=dtype
          )
  
      matrix_diagonals = array_ops_stack.stack(
          [superdiag, diag, subdiag], axis=-2
      )
      matrix = gen_array_ops.matrix_diag_v3(
          matrix_diagonals,
          k=(-1, 1),
          num_rows=-1,
          num_cols=-1,
          align="LEFT_RIGHT",
          padding_value=0.0,
      )
  
      if diagonals_format == "sequence":
          diagonals = [superdiag, diag, subdiag]
      elif diagonals_format == "compact":
          diagonals = array_ops_stack.stack(
              [superdiag, diag, subdiag], axis=-2
          )
      elif diagonals_format == "matrix":
          diagonals = matrix
  
      lin_op_diagonals = diagonals
  
      if use_placeholder:
          if diagonals_format == "sequence":
              lin_op_diagonals = [
                  array_ops.placeholder_with_default(d, shape=None)
                  for d in lin_op_diagonals
              ]
          else:
              lin_op_diagonals = array_ops.placeholder_with_default(
                  lin_op_diagonals, shape=None
              )
  
      operator = linalg_lib.LinearOperatorTridiag(
          diagonals=lin_op_diagonals,
          diagonals_format=diagonals_format,
          is_self_adjoint=True if ensure_self_adjoint_and_pd else None,
          is_positive_definite=True
          if ensure_self_adjoint_and_pd
          else None,
      )
      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 array_ops_stack
  from tensorflow.python.ops import gen_array_ops
  from tensorflow.python.ops import manip_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_test_util
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 for calling the _LinearOperatorTriDiagBase obj's underlying member method code with example usage: obj.build_operator_and_matrix(build_info, dtype, use_placeholder, ensure__adjoint_and_pd, diagonals_format) and returns: operator,  matrix