code: def multiplex(cond, a, b, name=None):
      """Return elements chosen from `a` or `b` depending on `cond`.
  
      This is similar to `np.where` and `tf.where` if `cond` and `a` are tensors.
      This is similar to `np.select` if `cond` and `a` are lists of tensors.
      In either case, this is simplified to only handle the case of dense tensors,
      no optional parameters, no broadcasting, etc..
  
      >>> multiplex([True, False, False, True], [1,2,3,4], [100,200,300,400])
      <tf.Tensor: shape=(4,), dtype=int32, numpy=array([  1, 200, 300,   4], ...)>
  
      >>> a1 = tf.constant([1, 2, 3, 4, 5], dtype=tf.int64)
      >>> a2 = tf.constant([6, 7, 8, 9, 10], dtype=tf.int64)
      >>> a3 = tf.constant([11, 12, 13, 14, 15], dtype=tf.int64)
      >>> b = tf.constant([101, 102, 103, 104, 105], dtype=tf.int64)
      >>> cond1 = tf.constant([False, False, True, False, False], dtype=bool)
      >>> cond2 = tf.constant([False, False, False, False, True], dtype=bool)
      >>> cond3 = tf.constant([True, False, True, False, True], dtype=bool)
      >>> multiplex_4_op.multiplex([cond1, cond2, cond3], [a1, a2, a3], b)
      <tf.Tensor: shape=(5,), ... numpy=array([ 11, 102,   3, 104,  10], ...)>
  
      Args:
        cond: tf.Tensor or list of tf.Tensor of type bool. Where True, yield `a`.
          When muliple corresponding `cond` elements are true, the first one yield
          based on the first one encountered.
        a: tf.Tensor or list of tf.Tensor, each with the same type and shape as `b`.
        b: tf.Tensor or list of tf.Tensor with the same type and shape as `a`. Yield
          `b` if all corresponding `cond` values is False.
        name: An optional name for the op.
  
      Returns:
        A tf.Tensor with elements from `a` where `cond` is True, and elements
        from `b` elsewhere.
      """
      if not isinstance(cond, (list, tuple)):
          # Support "old" use of multiplex where `cond` and `a` are tensors,
          # not lists of tensors.
          return examples_multiplex_dense(
              cond=[cond], a_values=[a], b_values=b, name=name
          )
      return examples_multiplex_dense(
          cond=cond, a_values=a, b_values=b, name=name
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: multiplex(cond, a, b, name) and returns: examples_multiplex_dense, examples_multiplex_dense