*Last updated: 2023-03-16.*
# tf_quant_finance.utils.dataclass
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Creates a data class object compatible with `tf.function`.
```python
tf_quant_finance.utils.dataclass(
cls
)
```
Modifies dunder methods of an input class with typed attributes to work as an
input/output to `tf.function`, as well as a loop variable of
`tf.while_loop`.
An intended use case for this decorator is on top of a simple class definition
with type annotated arguments like in the example below. It is not guaranteed
that this decorator works with an arbitrary class.
#### Examples
```python
import tensorflow as tf
import tf_quant_finance as tff
@tff.utils.dataclass
class Coords:
x: tf.Tensor
y: tf.Tensor
@tf.function
def fn(start_coords: Coords) -> Coords:
def cond(it, _):
return it < 10
def body(it, coords):
return it + 1, Coords(x=coords.x + 1, y=coords.y + 2)
return tf.while_loop(cond, body, loop_vars=(0, start_coords))[1]
start_coords = Coords(x=tf.constant(0), y=tf.constant(0))
fn(start_coords)
# Expected Coords(a=10, b=20)
```
#### Args:
* `cls`: Input class object with type annotated arguments. The class should not
have an init method defined. Class fields are treated as ordered in the
same order as they appear in the class definition.
#### Returns:
Modified class that can be used as a `tf.function` input/output as well
as a loop variable of `tf.function`. All typed arguments of the original
class are treated as ordered in the same order as they appear in the class
definition. All untyped arguments are ignored. Modified class modifies
`len` and `iter` methods defined for the class instances such that `len`
returns the number of arguments, and `iter` creates an iterator for the
ordered argument values.