class_docstr: Test for tri-diagonal matrix related ops.
code: def testPartialPivotingRaises(self):
      np.random.seed(0)
      batch_size = 8
      num_dims = 11
      num_rhs = 5
  
      diagonals_np = np.random.normal(
          size=(batch_size, 3, num_dims)
      ).astype(np.float32)
      rhs_np = np.random.normal(
          size=(batch_size, num_dims, num_rhs)
      ).astype(np.float32)
  
      with self.session() as sess, self.test_scope():
          with self.assertRaisesRegex(
              errors_impl.UnimplementedError,
              "Current implementation does not yet support pivoting.",
          ):
              diags = array_ops.placeholder(
                  shape=(batch_size, 3, num_dims), dtype=dtypes.float32
              )
              rhs = array_ops.placeholder(
                  shape=(batch_size, num_dims, num_rhs),
                  dtype=dtypes.float32,
              )
              sess.run(
                  linalg_impl.tridiagonal_solve(
                      diags, rhs, partial_pivoting=True
                  ),
                  feed_dict={diags: diagonals_np, rhs: rhs_np},
              )
  
imports: import numpy
  from tensorflow.compiler.tests import xla_test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradients
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.linalg import linalg_impl
  from tensorflow.python.platform import test
label_desc: Use image node_id 12 for calling the TridiagonalSolveOpsTest obj's underlying member method code with example usage: obj.testPartialPivotingRaises() without return types