fix lint
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/13346
Differential Revision: D12850686
Pulled By: michaelsuo
fbshipit-source-id: b7474d0a3f3347034592bef45125610c040cff6a
diff --git a/torch/nn/functional.py b/torch/nn/functional.py
index 05ec5ba..bb85b10 100644
--- a/torch/nn/functional.py
+++ b/torch/nn/functional.py
@@ -1170,7 +1170,8 @@
padding_idx (int, optional): If given, pads the output with the embedding vector at :attr:`padding_idx`
(initialized to zeros) whenever it encounters the index.
max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
- is renormalized to have norm :attr:`max_norm`. Note: this will modify :attr:`weight` in-place.
+ is renormalized to have norm :attr:`max_norm`.
+ Note: this will modify :attr:`weight` in-place.
norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
scale_grad_by_freq (boolean, optional): If given, this will scale gradients by the inverse of frequency of
the words in the mini-batch. Default ``False``.
@@ -1244,8 +1245,10 @@
offsets (LongTensor, optional): Only used when :attr:`input` is 1D. :attr:`offsets` determines
the starting index position of each bag (sequence) in :attr:`input`.
max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
- is renormalized to have norm :attr:`max_norm`. Note: this will modify :attr:`weight` in-place.
- norm_type (float, optional): The ``p`` in the ``p``-norm to compute for the :attr:`max_norm` option. Default ``2``.
+ is renormalized to have norm :attr:`max_norm`.
+ Note: this will modify :attr:`weight` in-place.
+ norm_type (float, optional): The ``p`` in the ``p``-norm to compute for the :attr:`max_norm` option.
+ Default ``2``.
scale_grad_by_freq (boolean, optional): if given, this will scale gradients by the inverse of frequency of
the words in the mini-batch. Default ``False``.
Note: this option is not supported when ``mode="max"``.
