code: def sparse_union_indices_and_values(x1, x2_indices, x2_values=None):
      """Compute the indices for the union of the indices of the provided
      `tf.SparseTensor`s and another set of indices and return the modified values
      for these indices.
  
      Args:
          x: a `tf.SparseTensor`.
          indices: another set of indices in the `tf.SparseTensor` format.
      Returns: A tuple containing:
          - the indices for the union
          - `x1` values for the union indices (some zeros were added)
          - `x2` values for the union indices (some zeros were added) or `None` if
            `x2_values` was `None`.
      """
      # Add zeros at the x2 indices to x1 to create the union.
      zeros2 = tf.SparseTensor(
          x2_indices,
          tf.zeros((tf.shape(x2_indices)[0],), x1.values.dtype),
          x1.dense_shape,
      )
      x1_for_union = tf.sparse.add(x1, zeros2)
      if x2_values is not None:
          # Add zeros at the x1 indices to x2 to create the union.
          x2 = tf.SparseTensor(x2_indices, x2_values, x1.dense_shape)
          zeros1 = tf.sparse.map_values(tf.zeros_like, x1)
          x2_for_union = tf.sparse.add(x2, zeros1)
          return (
              x1_for_union.indices,
              x1_for_union.values,
              x2_for_union.values,
          )
      else:
          return x1_for_union.indices, x1_for_union.values, None
  
file_path: keras/keras/backend/tensorflow/sparse.py
imports: import functools
  import tensorflow
label_desc: Use image node_id 6 for calling a global function with example usage: sparse_union_indices_and_values(x1, x2_indices, x2_values) and returns: x1_for_union,  x1_for_union,  x2_for_union, x1_for_union,  x1_for_union,  None