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### `tf.gradients(ys, xs, grad_ys=None, name='gradients', colocate_gradients_with_ops=False, gate_gradients=False, aggregation_method=None)` {#gradients}
Constructs symbolic partial derivatives of sum of `ys` w.r.t. x in `xs`.
`ys` and `xs` are each a `Tensor` or a list of tensors. `grad_ys`
is a list of `Tensor`, holding the gradients received by the
`ys`. The list must be the same length as `ys`.
`gradients()` adds ops to the graph to output the partial
derivatives of `ys` with respect to `xs`. It returns a list of
`Tensor` of length `len(xs)` where each tensor is the `sum(dy/dx)`
for y in `ys`.
`grad_ys` is a list of tensors of the same length as `ys` that holds
the initial gradients for each y in `ys`. When `grad_ys` is None,
we fill in a tensor of '1's of the shape of y for each y in `ys`. A
user can provide their own initial `grad_ys` to compute the
derivatives using a different initial gradient for each y (e.g., if
one wanted to weight the gradient differently for each value in
each y).
##### Args:
* <b>`ys`</b>: A `Tensor` or list of tensors to be differentiated.
* <b>`xs`</b>: A `Tensor` or list of tensors to be used for differentiation.
* <b>`grad_ys`</b>: Optional. A `Tensor` or list of tensors the same size as
`ys` and holding the gradients computed for each y in `ys`.
* <b>`name`</b>: Optional name to use for grouping all the gradient ops together.
defaults to 'gradients'.
* <b>`colocate_gradients_with_ops`</b>: If True, try colocating gradients with
the corresponding op.
* <b>`gate_gradients`</b>: If True, add a tuple around the gradients returned
for an operations. This avoids some race conditions.
* <b>`aggregation_method`</b>: Specifies the method used to combine gradient terms.
Accepted values are constants defined in the class `AggregationMethod`.
##### Returns:
A list of `sum(dy/dx)` for each x in `xs`.
##### Raises:
* <b>`LookupError`</b>: if one of the operations between `x` and `y` does not
have a registered gradient function.
* <b>`ValueError`</b>: if the arguments are invalid.
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