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Can this code implement one-dimensional convolution?
Thanks.

import torch
from condconv import CondConv2D

inp = torch.rand((2,3,12,12))

f = CondConv2D(3, 6, (3,3), stride=1,
                 padding=1, dilation=1, groups=1,
                 bias=True, padding_mode='replicate', num_experts=3, dropout_rate=0.2)

f(inp)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[354], line 1
----> 1 f(inp).shape

File ~/miniconda3/envs/license-plate/lib/python3.8/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
   1496 # If we don't have any hooks, we want to skip the rest of the logic in
   1497 # this function, and just call forward.
   1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1499         or _global_backward_pre_hooks or _global_backward_hooks
   1500         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501     return forward_call(*args, **kwargs)
   1502 # Do not call functions when jit is used
   1503 full_backward_hooks, non_full_backward_hooks = [], []

Cell In[348], line 82, in CondConv2D.forward(self, inputs)
     80     routing_weights = self._routing_fn(pooled_inputs)
     81     kernels = torch.sum(routing_weights[: ,None, None, None, None] * self.weight, 0)
---> 82     out = self._conv_forward(input, kernels)
     83     res.append(out)
     84 return torch.cat(res, dim=0)

Cell In[348], line 68, in CondConv2D._conv_forward(self, input, weight)
     66 def _conv_forward(self, input, weight):
     67     if self.padding_mode != 'zeros':
---> 68         return F.conv2d(F.pad(input, self._padding_repeated_twice, mode=self.padding_mode),
     69                         weight, self.bias, self.stride,
     70                         _pair(0), self.dilation, self.groups)
     71     return F.conv2d(input, weight, self.bias, self.stride,
     72                     self.padding, self.dilation, self.groups)

File ~/miniconda3/envs/license-plate/lib/python3.8/site-packages/torch/nn/modules/module.py:1614, in Module.__getattr__(self, name)
   1612     if name in modules:
   1613         return modules[name]
-> 1614 raise AttributeError("'{}' object has no attribute '{}'".format(
   1615     type(self).__name__, name))

AttributeError: 'CondConv2D' object has no attribute '_padding_repeated_twice'

def forward(self, x):
    x = self.condconv2d(x)