Add tensor.repeat docs. Remove legacy tensor repeat function. (pytorc…

…h#5666)

* Add tensor.repeat docs. Remove legacy tensor repeat function.

* Fix nit

torch/_tensor_docs.py

@@ -1261,6 +1261,35 @@ def callable(a, b) -> number
 In-place version of :meth:`~Tensor.renorm`
 """)
 
+add_docstr_all('repeat',
+               r"""
+repeat(*sizes) -> Tensor
+
+Repeats this tensor along the specified dimensions.
+
+Unlike :meth:`~Tensor.expand`, this function copies the tensor’s data.
+
+Args:
+    sizes (torch.Size or int...): The number of times to repeat this tensor along each
+        dimension
+
+Example::
+
+    >>> x = torch.Tensor([1, 2, 3])
+    >>> x.repeat(4, 2)
+
+     1  2  3  1  2  3
+     1  2  3  1  2  3
+     1  2  3  1  2  3
+     1  2  3  1  2  3
+    [torch.FloatTensor of size (4,6)]
+
+    >>> x.repeat(4, 2, 1).size()
+
+    torch.Size([4, 2, 3])
+
+""")
+
 add_docstr_all('resize_',
                r"""
 resize_(*sizes) -> Tensor

torch/_utils.py

@@ -259,54 +259,3 @@ def _take_tensors(tensors, size_limit):
     for buf, _ in buf_dict.values():
         if len(buf) > 0:
             yield buf
-
-
-def _repeat(self, *sizes):
-    r"""Repeats this tensor along the specified dimensions.
-
-    Unlike :meth:`expand`, this function copies the tensor's data.
-
-    Args:
-        *sizes (torch.Size or int...): The number of times to repeat this
-            tensor along each dimension
-
-    Example:
-        >>> x = torch.Tensor([1, 2, 3])
-        >>> x.repeat(4, 2)
-         1  2  3  1  2  3
-         1  2  3  1  2  3
-         1  2  3  1  2  3
-         1  2  3  1  2  3
-        [torch.FloatTensor of size 4x6]
-        >>> x.repeat(4, 2, 1).size()
-        torch.Size([4, 2, 3])
-    """
-    # If args == (torch.Size,), then we need to unpack the tuple
-    if len(sizes) == 1 and isinstance(sizes[0], torch.Size):
-        sizes = sizes[0]
-
-    repeats = list(sizes)
-
-    if len(repeats) < self.dim():
-        raise ValueError('Number of dimensions of repeat dims can not be '
-                         'smaller than number of dimensions of tensor')
-
-    # Add new leading dimensions to the tensor if the
-    # number of target dimensions is larger than the
-    # number of source dimensions.
-    num_new_dimensions = len(repeats) - self.dim()
-    padded_size = [1] * num_new_dimensions + list(self.size())
-    target_size = torch.Size([a * b for a, b in zip(padded_size, repeats)])
-
-    xtensor = self.new().set_(self)
-    xtensor = xtensor.expand(padded_size)
-
-    result = self.new()
-    result.resize_(target_size)
-    urtensor = result.new(result)
-    for i in range(xtensor.dim()):
-        urtensor = urtensor.unfold(i, xtensor.size(i), xtensor.size(i))
-
-    urtensor.copy_(xtensor.expand_as(urtensor))
-
-    return result