code: def testConjugateGradientMultipleRHSPlaceholders(self, n, rhs):
      # Test the case where a_mat and b_mat are placeholders and they have unknown
      # dimension values.
  
      if tf.executing_eagerly():
          return
  
      x_obs = tf.constant(
          np.random.rand(n, 2), dtype=tf.float32, shape=[n, 2]
      )
      a_mat = tf.eye(n) + tf.matmul(
          x_obs, tf.linalg.matrix_transpose(x_obs)
      )
      a_mat_ph = tf.compat.v1.placeholder(
          tf.float32, shape=(None, None)
      )
      a_mat_value = self.evaluate(a_mat)
  
      x_exact = tf.constant(
          np.random.rand(n, rhs), dtype=tf.float32, shape=[n, rhs]
      )
      b_mat = tf.matmul(a_mat, x_exact)
      b_mat_ph = tf.compat.v1.placeholder(
          tf.float32, shape=(None, None)
      )
      b_mat_value = self.evaluate(b_mat)
  
      x_exact_numpy = self.evaluate(x_exact)
      with self.cached_session() as sess:
          x_approx = linalg.conjugate_gradient(a_mat_ph, b_mat_ph)
          x_approx_value = sess.run(
              x_approx,
              feed_dict={a_mat_ph: a_mat_value, b_mat_ph: b_mat_value},
          )
          self.assertAllClose(
              x_exact_numpy, x_approx_value, rtol=1e-4, atol=1e-4
          )
  
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 3 for calling the ConjugateGradientTest obj's underlying member method code with example usage: obj.testConjugateGradientMultipleRHSPlaceholders(n, rhs) without return types