*Last updated: 2023-03-16.*
# tf_quant_finance.math.gradients
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Computes the gradients of `func_or_y` wrt to `*xs`.
```python
tf_quant_finance.math.gradients(
func_or_y, xs, output_gradients=None, use_gradient_tape=False,
unconnected_gradients=None, name=None
)
```
#### Args:
* `func_or_y`: Either a `Tensor` connected to the input `x` or a Python callable
accepting one `Tensor` of shape of `x` and returning a `Tensor` of any
shape. The function whose gradient is to be computed. If eagerly
executing, can only be a callable, i.e., one should not supply a Tensor
in eager mode.
xs: Python list of parameters of `f` for which to differentiate. (Can also
be single `Tensor`.)
output_gradients: A `Tensor` or list of `Tensor`s the same size as the
result `ys = f(*xs)` and holding the gradients computed for each `y` in
`ys`. This argument is forwarded to the underlying gradient implementation
(i.e., either the `grad_ys` argument of `tf.gradients` or the
`output_gradients` argument of `tf.GradientTape.gradient`).
Default value: `None` which maps to a ones-like `Tensor` of `ys`.
use_gradient_tape: Python `bool` indicating that `tf.GradientTape` should be
used regardless of `tf.executing_eagerly()` status.
Default value: `False`.
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., 'gradients').
#### Returns:
A `Tensor` with the gradient of `y` wrt each of `xs` or a list of `Tensor`s
if `xs` is a list.