*Last updated: 2023-03-16.*
# tf_quant_finance.math.value_and_jacobian
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Computes `f(x)` and its jacobian wrt to `x`.
```python
tf_quant_finance.math.value_and_jacobian(
f, x, unconnected_gradients=None, name=None, parallel_iterations=None,
experimental_use_pfor=True
)
```
#### Args:
* `f`: Python `callable` to be differentiated. If `f` returns a scalar, this
scalar will be differentiated. If `f` returns a tensor or list of
tensors, by default a scalar will be computed by adding all their values
to produce a single scalar. If desired, the tensors can be elementwise
multiplied by the tensors passed as the `dy` keyword argument to the
returned jacobian function.
* `x`: A `Tensor` with respect to which the gradient is to be computed.
* `unconnected_gradients`: An enum `tf.UnconnectedGradients` which specifies
the gradient value returned when the given input tensors are
unconnected. Default value: `None`, which maps to
`tf.UnconnectedGradients.NONE`.
* `name`: Python `str` name prefixed to ops created by this function.
Default value: `None` (i.e., `'value_and_jacobian'`).
* `parallel_iterations`: A knob to control how many iterations are dispatched
in parallel. This knob can be used to control the total memory usage.
* `experimental_use_pfor`: If true, uses pfor for computing the Jacobian.
Else uses a tf.while_loop.
#### Returns:
A tuple of two elements. The first one is a `Tensor` representing the value
of the function at `x` and the second one is a `Tensor` representing
jacobian of `f(x)` wrt `x`.
* `y`: `y = f(x)`.
* `dydx`: Jacobian of `y` wrt `x_i`, where `x_i` is the i-th parameter in
`x`.