class_docstr: Tests clip_and_aggreate_gradients.
docstr: Returns the loss and variable tensors or callables.
docstr_tok: ["Returns","the","loss","and","variable","tensors","or","callables","."]
code: def _get_loss_and_vars(self, n, keepdims=False, is_callable=False):
      """Returns the loss and variable tensors or callables."""
      # The "model" here consists of both sparse and dense parameters to make sure
      # `clip_and_aggregate_gradients` computes the gradients in the correct way
      # and in the right format. The sparse layer is the embedding layer `emb0`,
      # from which multiple embeddings are gathered, with indices stored
      # in `ind0`. And the dense parameters is the variable var1 which is directly
      # used. The loss is the quadratic loss between the model output and the
      # data stored in `data0` and `data1`. We also add a dummy variable
      # `dummy_var` which does not participate in the loss computation to test
      # the `unconnected` argument.
      emb0 = tf.keras.layers.Embedding(
          4,
          2,
          embeddings_initializer=tf.keras.initializers.Constant(
              np.array([[0.0, 0.0], [1.0, 1.0], [2.0, 2.0], [3.0, 3.0]])
          ),
      )
      ind0 = tf.constant([1, 1, 2, 3, 2])
      data0 = tf.constant(
          [
              [3.0, 4.0],
              [5.0, 6.0],
              [7.0, 8.0],
              [-1.0, 0.0],
              [-2.0, -1.0],
              [-3.0, -2.0],
          ]
      )
  
      var1 = tf.Variable([[1.0], [1.0], [2.0], [2.0], [3.0], [3.0]])
      data1 = tf.constant(
          [[-1.0], [-2.0], [-2.0], [-3.0], [-3.0], [-4.0]]
      )
  
      dummy_var = tf.Variable(np.array([[1.0]]).astype(np.float64))
  
      def _loss(val0, val1):
          return 0.5 * tf.reduce_sum(
              input_tensor=tf.math.squared_difference(val0, val1),
              axis=1,
          )
  
      if is_callable:
  
          def loss_fn():
              val0 = tf.concat(
                  [emb0(ind0), tf.constant([[0.0, 0.0]])], axis=0
              )
              return tf.reduce_sum(
                  tf.reshape(
                      _loss(data0, val0) + _loss(data1, var1), [n, -1]
                  ),
                  keepdims=keepdims,
                  axis=1,
              )
  
          def vars_fn():
              return (emb0.embeddings, var1, dummy_var)
  
          return loss_fn, vars_fn
      else:
          val0 = tf.concat(
              [emb0(ind0), tf.constant([[0.0, 0.0]])], axis=0
          )
          loss = tf.reduce_sum(
              tf.reshape(
                  _loss(data0, val0) + _loss(data1, var1), [n, -1]
              ),
              keepdims=keepdims,
              axis=1,
          )
          return loss, (emb0.embeddings, var1, dummy_var)
  
file_path: privacy/tensorflow_privacy/privacy/optimizers/clip_and_aggregate_gradients_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_privacy.privacy.optimizers import clip_and_aggregate_gradients
label_desc: Use image node_id 1 for calling the ClipAndAggregateGradientsTest obj's underlying member method code with example usage: obj._get_loss_and_vars(n, keepdims, is_callable) and returns: unknown, loss_fn,  vars_fn, loss,  emb0,  var1,  dummy_var, tf, emb0,  var1,  dummy_var