*Last updated: 2023-03-16.*
# tf_quant_finance.math.jacobian
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Computes the jacobian of `func` wrt to `x`.
```python
tf_quant_finance.math.jacobian(
func, x, unconnected_gradients=None, parallel_iterations=None,
experimental_use_pfor=True, name=None
)
```
#### Args:
* `func`: Python callable accepting one `Tensor` of shape of `x` and returning
a `Tensor` of any shape. The function whose jacobian is to be computed.
* `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`.
* `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.
* `name`: Python `str` name prefixed to ops created by this function.
Default value: `None` (i.e., 'jacobian').
#### Returns:
A `Tensor` with the gradient of `y` wrt each of `x`.