code: def sparse_intersection_indices_and_values(x1, x2):
      """Compute the indices for the intersection of two `tf.SparseTensor`s and
      modify the values for these indices.
  
      Args:
          x1: the first `tf.SparseTensor`.
          x2: the second `tf.SparseTensor`.
      Returns: A tuple containing:
          - the indices for the intersection
          - `x1` values for the intersection indices (some values were removed)
          - `x2` values for the intersection indices (some values were removed)
      """
      # Compute the intersection of indices in the form of a sparse
      # tensor containing ones as values.
      ones1 = tf.sparse.map_values(ones_like_int8, x1)
      ones2 = tf.sparse.map_values(ones_like_int8, x2)
      # tf.sets.intersection ignores the last dimension when, so we
      # need to add a dummy extra dimension and then remove it.
      intersection_extra_dim = tf.sets.intersection(
          tf.sparse.expand_dims(ones1, axis=-1),
          tf.sparse.expand_dims(ones2, axis=-1),
      )
  
      def empty_intersection():
          return (
              empty_tensor((0, x1.shape.rank), dtype=tf.int64),
              empty_tensor((0,), dtype=x1.values.dtype),
              empty_tensor((0,), dtype=x2.values.dtype),
          )
  
      def non_empty_intersection():
          intersection = tf.sparse.reshape(
              intersection_extra_dim, x1.dense_shape
          )
  
          # Compute the masks to remove indices in x1 and x2 that are not
          # in the intersection, then trim x1 and x2.
          zeros1 = tf.sparse.map_values(zeros_like_int8, x1)
          zeros2 = tf.sparse.map_values(zeros_like_int8, x2)
          mask1 = tf.sparse.add(zeros1, intersection)
          mask2 = tf.sparse.add(zeros2, intersection)
          return (
              intersection.indices,
              tf.sparse.retain(
                  x1, tf.cast(mask1.values, tf.bool)
              ).values,
              tf.sparse.retain(
                  x2, tf.cast(mask2.values, tf.bool)
              ).values,
          )
  
      return tf.cond(
          tf.equal(tf.size(intersection_extra_dim), 0),
          empty_intersection,
          non_empty_intersection,
      )
  
file_path: keras/keras/backend/tensorflow/sparse.py
imports: import functools
  import tensorflow
label_desc: Use image node_id 8 for calling a global function with example usage: sparse_intersection_indices_and_values(x1, x2) and returns: tf, empty_tensor,  empty_tensor,  empty_tensor, intersection,  tf,  tf