code: def testNDimensional(self):
      with self.test_scope():
          shape = [77, 10, 3, 5, 7]
          superdiag = random.stateless_random_uniform(
              shape=shape[:-1], dtype=dtypes.float32, seed=[5, 10]
          )
          maindiag = random.stateless_random_uniform(
              shape=shape[:-1], dtype=dtypes.float32, seed=[5, 11]
          )
          subdiag = random.stateless_random_uniform(
              shape=shape[:-1], dtype=dtypes.float32, seed=[5, 12]
          )
          rhs = random.stateless_random_uniform(
              shape=shape, dtype=dtypes.float32, seed=[5, 13]
          )
  
          expected = self._tridiagonal_matmul(
              (superdiag, maindiag, subdiag),
              rhs,
              diagonals_format="sequence",
          )
  
          real = self._jit_tridiagonal_matmul(
              (superdiag, maindiag, subdiag),
              rhs,
              diagonals_format="sequence",
          )
  
          self.assertAllClose(expected, real)
  
imports: import numpy
  from tensorflow.compiler.tests import xla_test
  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 ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import gradient_checker_v2
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import stateless_random_ops
  from tensorflow.python.ops.linalg import linalg_impl
  from tensorflow.python.platform import googletest
label_desc: Use image node_id 8 for calling the TridiagonalMatMulOpsTest obj's underlying member method code with example usage: obj.testNDimensional() without return types