class_docstr: Separable Conv2D in pytorch
  
  Args:
      in_c (`int`):
          Number of channels in the input image.
      out_c (`int`):
          Number of channels produced by the convolution.
      kernel_size (`Union[int, Tuple]`):
          Size of the convolving kernel.
      stride (`Union[int, Tuple]`):
          Stride of the convolution.
      in_f (`int`):
          Padding parameter.
      in_t (`int`):
          Padding parameter.
      fuller (`bool=False`):
          Whether to use group in conv2 layer.
      activation (`nn.Module=nn.ReLU()`):
          Activation layer.
      relu_after_bn (`bool=False`):
          Whether to add ReLU after batch norm.
code: def hack(self):
      self.hacked = not self.hacked
      with torch.no_grad():
          self.conv1.weight.set_(self.conv1.weight.flip([2, 3]))
          self.ln1.weight.set_(self.ln1.weight.flip([1, 2]))
          self.ln1.bias.set_(self.ln1.bias.flip([1, 2]))
          self.conv2.weight.set_(self.conv2.weight.flip([2, 3]))
          self.ln2.weight.set_(self.ln2.weight.flip([1, 2]))
          self.ln2.bias.set_(self.ln2.bias.flip([1, 2]))
          if self.hacked:
              self.conv1.padding = self.pad1.padding[3::-2]
              self.conv2.padding = self.pad2.padding[3::-2]
          else:
              self.conv1.padding = (0, 0)
              self.conv2.padding = (0, 0)
  
imports: import torch
  from torch import nn
  from typing import Union, Tuple
label_desc: Use image node_id 2 for calling the SeparableConv2d obj's underlying member method code with example usage: obj.hack() without return types