fix docs (#12126)

Summary:
- fix https://github.com/pytorch/pytorch/issues/12120
- add `torch.argsort`, `torch.pdist`, `broadcast_tensors` to *.rst files
- add parameter dim to `torch.unique` doc
- fix table and args for `torch.norm`
- test plan: make html and check docs in browser

gchanan
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12126

Differential Revision: D10087006

Pulled By: weiyangfb

fbshipit-source-id: 25f65c43d14e02140d0da988d8742c7ade3d8cc9

1 files changed, 15 insertions, 11 deletions

diff --git a/torch/functional.py b/torch/functional.py
index 0eac8f1674..47625171c5 100644
--- a/torch/functional.py
+++ b/torch/functional.py
@@ -439,6 +439,8 @@ def unique(input, sorted=False, return_inverse=False, dim=None):
             before returning as output.
         return_inverse (bool): Whether to also return the indices for where
             elements in the original input ended up in the returned unique list.
+        dim (int): the dimension to apply unique. If ``None``, the unique of the
+            flattened input is returned. default: ``None``
 
     Returns:
         (Tensor, Tensor (optional)): A tensor or a tuple of tensors containing
@@ -646,8 +648,9 @@ def norm(input, p="fro", dim=None, keepdim=False, out=None):
 
     Args:
         input (Tensor): the input tensor
-        p ({int, float, inf, -inf, 'fro', 'nuc'}): the order of norm
+        p (int, float, inf, -inf, 'fro', 'nuc'): the order of norm
             The following norms can be calculated:
+
             =====  ============================  ==========================
             ord    matrix norm                   vector norm
             =====  ============================  ==========================
@@ -656,18 +659,19 @@ def norm(input, p="fro", dim=None, keepdim=False, out=None):
             'nuc'  nuclear norm                  --
             Other  as vec norm when dim is None  sum(abs(x)**ord)**(1./ord)
             =====  ============================  ==========================
-        dim ({int, 2-tuple of ints, 2-list of ints}, optional): If it is an int,
-        vector norm will be calculated, if it is 2-tuple of ints, matrix norm
-        will be calculated. If the value is None, matrix norm will be calculated
-        when the input tensor only has two dimensions, vector norm will be
-        calculated when the input tensor only has one dimension. If the input
-        tensor has more than two dimensions, the vector norm will be applied to
-        last dimension.
+
+        dim (int, 2-tuple of ints, 2-list of ints, optional): If it is an int,
+            vector norm will be calculated, if it is 2-tuple of ints, matrix norm
+            will be calculated. If the value is None, matrix norm will be calculated
+            when the input tensor only has two dimensions, vector norm will be
+            calculated when the input tensor only has one dimension. If the input
+            tensor has more than two dimensions, the vector norm will be applied to
+            last dimension.
         keepdim (bool): whether the output tensors have :attr:`dim`
-            retained or not. Ignored if attr:`dim`=``None`` and
-            :attr:`out`=``None``.
+            retained or not. Ignored if :attr:`dim` = ``None`` and
+            :attr:`out` = ``None``.
         out (Tensor, optional): the output tensor. Ignored if
-        attr:`dim`=``None`` and :attr:`out`=``None``.
+            :attr:`dim` = ``None`` and :attr:`out` = ``None``.
 
     Example::
         >>> import torch