import torch.nn as nn
import torch.nn.functional as F

from collections import OrderedDict
from torch.nn.modules.utils import _single, _pair, _triple
from torchmeta.modules.module import MetaModule

class MetaConv1d(nn.Conv1d, MetaModule):
    __doc__ = nn.Conv1d.__doc__

    def forward(self, input, params=None):
        if params is None:
            params = OrderedDict(self.named_parameters())
        bias = params.get('bias', None)
        return self._conv_forward(input, params['weight'], bias)


class MetaConv2d(nn.Conv2d, MetaModule):
    __doc__ = nn.Conv2d.__doc__

    def forward(self, input, params=None):
        if params is None:
            params = OrderedDict(self.named_parameters())
        bias = params.get('bias', None)
        return self._conv_forward(input, params['weight'], bias)


class MetaConv3d(nn.Conv3d, MetaModule):
    __doc__ = nn.Conv3d.__doc__

    def forward(self, input, params=None):
        if params is None:
            params = OrderedDict(self.named_parameters())
        bias = params.get('bias', None)
        return self._conv_forward(input, params['weight'], bias)

import torch.nn as nn
import torch.nn.functional as F

from collections import OrderedDict
from torch.nn.modules.utils import _single, _pair, _triple
from torchmeta.modules.module import MetaModule

class MetaConv1d(nn.Conv1d, MetaModule):
    __doc__ = nn.Conv1d.__doc__

    def forward(self, input, params=None):
        if params is None:
            params = OrderedDict(self.named_parameters())
        bias = params.get('bias', None)
        return self._conv_forward(input, params['weight'], bias)


class MetaConv2d(nn.Conv2d, MetaModule):
    __doc__ = nn.Conv2d.__doc__

    def forward(self, input, params=None):
        if params is None:
            params = OrderedDict(self.named_parameters())
        bias = params.get('bias', None)
        return self._conv_forward(input, params['weight'], bias)


class MetaConv3d(nn.Conv3d, MetaModule):
    __doc__ = nn.Conv3d.__doc__

    def forward(self, input, params=None):
        if params is None:
            params = OrderedDict(self.named_parameters())
        bias = params.get('bias', None)
        return self._conv_forward(input, params['weight'], bias)
