parameters: (data, num_clusters, random_seed)
return_statement: return clusters_init
docstring: Does random initialization of clusters.
  
    Args:
      data: a list of Tensors with a matrix of data, each row is an example.
      num_clusters: an integer with the number of clusters.
      random_seed: Seed for PRNG used to initialize seeds.
  
    Returns:
      A Tensor with num_clusters random rows of data.
docstring_summary: Does random initialization of clusters.
docstring_tokens: Does random initialization of clusters .
function: def _init_clusters_random(data, num_clusters, random_seed):
    """Does random initialization of clusters.
  
    Args:
      data: a list of Tensors with a matrix of data, each row is an example.
      num_clusters: an integer with the number of clusters.
      random_seed: Seed for PRNG used to initialize seeds.
  
    Returns:
      A Tensor with num_clusters random rows of data.
    """
    assert isinstance(data, list)
    num_data = tf.add_n([tf.shape(inp)[0] for inp in data])
    with tf.control_dependencies([tf.assert_less_equal(num_clusters, num_data)]):
      indices = tf.random_uniform([num_clusters],
                                  minval=0,
                                  maxval=tf.cast(num_data, tf.int64),
                                  seed=random_seed,
                                  dtype=tf.int64)
    indices = tf.cast(indices, tf.int32) % num_data
    clusters_init = embedding_lookup(data, indices, partition_strategy='div')
    return clusters_init
function_tokens: def _init_clusters_random ( data , num_clusters , random_seed ) : assert isinstance ( data , list ) num_data = tf . add_n ( [ tf . shape ( inp ) [ 0 ] for inp in data ] ) with tf . control_dependencies ( [ tf . assert_less_equal ( num_clusters , num_data ) ] ) : indices = tf . random_uniform ( [ num_clusters ] , minval = 0 , maxval = tf . cast ( num_data , tf . int64 ) , seed = random_seed , dtype = tf . int64 ) indices = tf . cast ( indices , tf . int32 ) % num_data clusters_init = embedding_lookup ( data , indices , partition_strategy = 'div' ) return clusters_init