code: def test_minimize_with_2D_indicies_for_embedding_lookup_frobenius():
      # This test invokes the ResourceSparseApplyConditionalGradient
      # operation.
      var0 = tf.Variable(tf.ones([2, 2]))
  
      def loss():
          return tf.math.reduce_sum(tf.nn.embedding_lookup(var0, [[1]]))
  
      # the gradient for this loss function:
      grads0 = tf.constant([[0, 0], [1, 1]], dtype=tf.float32)
      norm0 = tf.math.reduce_sum(grads0**2) ** 0.5
  
      learning_rate = 0.1
      lambda_ = 0.1
      ord = "fro"
      opt = cg_lib.ConditionalGradient(
          learning_rate=learning_rate, lambda_=lambda_, ord=ord
      )
      _ = opt.minimize(loss, var_list=[var0])
  
      # Run 1 step of cg_op
      test_utils.assert_allclose_according_to_type(
          [
              [1, 1],
              [
                  learning_rate * 1
                  - (1 - learning_rate) * lambda_ * 1 / norm0,
                  learning_rate * 1
                  - (1 - learning_rate) * lambda_ * 1 / norm0,
              ],
          ],
          var0.numpy(),
      )
  
imports: import numpy
  import pytest
  import platform
  import tensorflow
  from tensorflow_addons.utils import test_utils
  from tensorflow_addons.optimizers import conditional_gradient
label_desc: Use image node_id 11 for calling a global function with example usage: test_minimize_with_2D_indicies_for_embedding_lookup_frobenius() and returns: tf