code: def testAInvUpdate(self, batch_size, context_dim):
      a_array = 2 * np.eye(context_dim) + np.array(
          range(context_dim * context_dim)
      ).reshape((context_dim, context_dim))
      a_array = a_array + a_array.T
      a_inv_array = np.linalg.inv(a_array)
      x_array = np.array(range(batch_size * context_dim)).reshape(
          (batch_size, context_dim)
      )
      expected_a_inv_updated_array = np.linalg.inv(
          a_array + np.matmul(np.transpose(x_array), x_array)
      )
  
      a_inv = tf.constant(
          a_inv_array,
          dtype=tf.float32,
          shape=[context_dim, context_dim],
      )
      x = tf.constant(
          x_array, dtype=tf.float32, shape=[batch_size, context_dim]
      )
      a_inv_update = linalg.update_inverse(a_inv, x)
      self.assertAllClose(
          expected_a_inv_updated_array,
          self.evaluate(a_inv + a_inv_update),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.bandits.policies import linalg
label_desc: Use image node_id 1 for calling the LinalgTest obj's underlying member method code with example usage: obj.testAInvUpdate(batch_size, context_dim) without return types