class_docstr: Test for tri-diagonal matrix related ops.
code: def _makeDataForGradientWithBatching(self):
      y = np.array([1, 3, 2, 4]).astype(np.float32)
      grad_diags = np.array(
          [[-5, 0, 4, 0], [9, 0, -4, -16], [0, 0, 5, 16]]
      ).astype(np.float32)
      grad_rhs = np.array([1, 0, -1, 4]).astype(np.float32)
  
      diags_batched = np.array(
          [
              [_sample_diags, 2 * _sample_diags, 3 * _sample_diags],
              [4 * _sample_diags, 5 * _sample_diags, 6 * _sample_diags],
          ]
      ).astype(np.float32)
      rhs_batched = np.array(
          [
              [_sample_rhs, -_sample_rhs, _sample_rhs],
              [-_sample_rhs, _sample_rhs, -_sample_rhs],
          ]
      ).astype(np.float32)
      y_batched = np.array([[y, y, y], [y, y, y]]).astype(np.float32)
      expected_grad_diags_batched = np.array(
          [
              [grad_diags, -grad_diags / 4, grad_diags / 9],
              [-grad_diags / 16, grad_diags / 25, -grad_diags / 36],
          ]
      ).astype(np.float32)
      expected_grad_rhs_batched = np.array(
          [
              [grad_rhs, grad_rhs / 2, grad_rhs / 3],
              [grad_rhs / 4, grad_rhs / 5, grad_rhs / 6],
          ]
      ).astype(np.float32)
  
      return (
          y_batched,
          diags_batched,
          rhs_batched,
          expected_grad_diags_batched,
          expected_grad_rhs_batched,
      )
  
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 33 for calling the TridiagonalSolveOpsTest obj's underlying member method code with example usage: obj._makeDataForGradientWithBatching() and returns: y_batched,  diags_batched,  rhs_batched,  expected_grad_diags_batched,  expected_grad_rhs_batched