Remove unnecessary typing dependency. (#16776)

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

torch/functional.py

@@ -7,7 +7,6 @@
 from itertools import product
 import math
 import warnings
-from typing import Optional, Tuple, List, Union
 
 __all__ = [
     'argmax',
@@ -31,7 +30,6 @@
 ]
 
 
-@annotate(ret=List[torch.Tensor], tensors=torch.Tensor)
 def broadcast_tensors(*tensors):
     r"""broadcast_tensors(*tensors) -> List of Tensors
 
@@ -54,8 +52,6 @@ def broadcast_tensors(*tensors):
     return torch._C._VariableFunctions.broadcast_tensors(tensors)
 
 
-@annotate(ret=List[torch.Tensor], tensor=torch.Tensor,
-          split_size_or_sections=Union[List[int], int], dim=int)
 def split(tensor, split_size_or_sections, dim=0):
     r"""Splits the tensor into chunks.
 
@@ -81,10 +77,6 @@ def split(tensor, split_size_or_sections, dim=0):
     return tensor.split(split_size_or_sections, dim)
 
 
-@annotate(ret=Tuple[torch.Tensor, torch.Tensor],
-          A=torch.Tensor,
-          info=Optional[torch.Tensor],
-          pivot=bool)
 def btrifact(A, info=None, pivot=True):
     r"""Batch LU factorization.
 
@@ -132,9 +124,6 @@ def btrifact(A, info=None, pivot=True):
     return A.btrifact(info, pivot)
 
 
-@annotate(ret=Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]],
-          LU_data=torch.Tensor, LU_pivots=torch.Tensor,
-          unpack_data=bool, unpack_pivots=bool)
 def btriunpack(LU_data, LU_pivots, unpack_data=True, unpack_pivots=True):
     r"""Unpacks the data and pivots from a batched LU factorization (btrifact) of a tensor.
 
@@ -179,9 +168,6 @@ def btriunpack(LU_data, LU_pivots, unpack_data=True, unpack_pivots=True):
     return P, L, U
 
 
-@annotate(ret=torch.Tensor,
-          equation=str,
-          operands=torch.Tensor)
 def einsum(equation, *operands):
     r"""einsum(equation, *operands) -> Tensor