# Can I apply tf.map_fn(…) to multiple inputs/outputs?

## Issue

``````a = tf.constant([[1,2,3],[4,5,6]])
b = tf.constant([True, False], dtype=tf.bool)

a.eval()
array([[1, 2, 3],
[4, 5, 6]], dtype=int32)
b.eval()
array([ True, False], dtype=bool)
``````

I want to apply a functions to the inputs above, `a`, and `b` using `tf.map_fn`. It will input both `[1,2,3]`, and `True` and output similar values.

Let’s say out function is simply the identity: `lambda(x,y): x,y` so, given an input of `[1,2,3], True`, it will output those identical tensors.

I know how to use `tf.map_fn(...)` with one variable, but not with two. And in this case I have mixed data types (int32 and bool) so I can’t simply concatenate the tensors and split them after the call.

Can I use `tf.map_fn(...)` with multiple inputs/outputs of different data types?

## Solution

Figured it out. You have to define the data types for each tensor in `dtype` for each of the different tensors, then you can pass the tensors as a tuple, your map function receives a tuple of inputs, and `map_fn` returns back back a tuple.

Example that works:

``````a = tf.constant([[1,2,3],[4,5,6]])
b = tf.constant([True, False], dtype=tf.bool)

c = tf.map_fn(lambda x: (x[0], x[1]), (a,b), dtype=(tf.int32, tf.bool))

c[0].eval()
array([[1, 2, 3],
[4, 5, 6]], dtype=int32)
c[1].eval()
array([ True, False], dtype=bool)
``````