import vision.torchvision.transforms as transforms
 
img_to_pad = transforms.Compose([
    transforms.Pad(padding=2, padding_mode='symmetric'),
    transforms.ToTensor(),
   ])

import torch.nn.functional as F
 
feature = feature.unsqueeze(0).unsqueeze(0)
avg_feature = F.pad(feature, pad = [1, 1, 1, 1], mode='replicate')

@torch._jit_internal.weak_script
def pad(input, pad, mode='constant', value=0):
 # type: (Tensor, List[int], str, float) -> Tensor
 r"""Pads tensor.
 Pading size:
  The number of dimensions to pad is :math:`\left\lfloor\frac{\text{len(pad)}}{2}\right\rfloor`
  and the dimensions that get padded begins with the last dimension and moves forward.
  For example, to pad the last dimension of the input tensor, then `pad` has form
  `(padLeft, padRight)`; to pad the last 2 dimensions of the input tensor, then use
  `(padLeft, padRight, padTop, padBottom)`; to pad the last 3 dimensions, use
  `(padLeft, padRight, padTop, padBottom, padFront, padBack)`.
 Padding mode:
  See :class:`torch.nn.ConstantPad2d`, :class:`torch.nn.ReflectionPad2d`, and
  :class:`torch.nn.ReplicationPad2d` for concrete examples on how each of the
  padding modes works. Constant padding is implemented for arbitrary dimensions.
  Replicate padding is implemented for padding the last 3 dimensions of 5D input
  tensor, or the last 2 dimensions of 4D input tensor, or the last dimension of
  3D input tensor. Reflect padding is only implemented for padding the last 2
  dimensions of 4D input tensor, or the last dimension of 3D input tensor.
 .. include:: cuda_deterministic_backward.rst
 Args:
  input (Tensor): `Nd` tensor
  pad (tuple): m-elem tuple, where :math:`\frac{m}{2} \leq` input dimensions and :math:`m` is even.
  mode: 'constant', 'reflect' or 'replicate'. Default: 'constant'
  value: fill value for 'constant' padding. Default: 0
 Examples::
  > t4d = torch.empty(3, 3, 4, 2)
  > p1d = (1, 1) # pad last dim by 1 on each side
  > out = F.pad(t4d, p1d, "constant", 0) # effectively zero padding
  > print(out.data.size())
  torch.Size([3, 3, 4, 4])
  > p2d = (1, 1, 2, 2) # pad last dim by (1, 1) and 2nd to last by (2, 2)
  > out = F.pad(t4d, p2d, "constant", 0)
  > print(out.data.size())
  torch.Size([3, 3, 8, 4])
  > t4d = torch.empty(3, 3, 4, 2)
  > p3d = (0, 1, 2, 1, 3, 3) # pad by (0, 1), (2, 1), and (3, 3)
  > out = F.pad(t4d, p3d, "constant", 0)
  > print(out.data.size())
  torch.Size([3, 9, 7, 3])
 """
 assert len(pad) % 2 == 0, 'Padding length must be divisible by 2'
 assert len(pad) // 2 <= input.dim(), 'Padding length too large'
 if mode == 'constant':
  ret = _VF.constant_pad_nd(input, pad, value)
 else:
  assert value == 0, 'Padding mode "{}"" doesn\'t take in value argument'.format(mode)
  if input.dim() == 3:
   assert len(pad) == 2, '3D tensors expect 2 values for padding'
   if mode == 'reflect':
    ret = torch._C._nn.reflection_pad1d(input, pad)
   elif mode == 'replicate':
    ret = torch._C._nn.replication_pad1d(input, pad)
   else:
    ret = input # TODO: remove this when jit raise supports control flow
    raise NotImplementedError
 
  elif input.dim() == 4:
   assert len(pad) == 4, '4D tensors expect 4 values for padding'
   if mode == 'reflect':
    ret = torch._C._nn.reflection_pad2d(input, pad)
   elif mode == 'replicate':
    ret = torch._C._nn.replication_pad2d(input, pad)
   else:
    ret = input # TODO: remove this when jit raise supports control flow
    raise NotImplementedError
 
  elif input.dim() == 5:
   assert len(pad) == 6, '5D tensors expect 6 values for padding'
   if mode == 'reflect':
    ret = input # TODO: remove this when jit raise supports control flow
    raise NotImplementedError
   elif mode == 'replicate':
    ret = torch._C._nn.replication_pad3d(input, pad)
   else:
    ret = input # TODO: remove this when jit raise supports control flow
    raise NotImplementedError
  else:
   ret = input # TODO: remove this when jit raise supports control flow
   raise NotImplementedError("Only 3D, 4D, 5D padding with non-constant padding are supported for now")
 return ret