I am facing similar kind of issue but not while creating the layers rather while calling forward.
Below is the code.
class BasicBlock(nn.Module):
    expansion = 1
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1):
        super(BasicBlock, self).__init__()
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self,x,sample=False):
        identity = x

        out = self.conv1(x,sample)
        out = self.relu(out)
        out = self.conv2(out,sample)

        if self.downsample is not None:
            identity = self.downsample(x,sample)

        out += identity
        out = self.relu(out)

        return out

class ResNet(nn.Module):

    def __init__(self, block, layers, init='xavierUniform', num_classes=7, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group

        self.conv1 = BayesianConv(3,self.inplanes,kernel_size=7, stride=2, padding=3,bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = BayesianLinear(512*block.expansion,num_classes)

        weights_init_xavierUniform(self)

        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation))

        return nn.Sequential(*layers)
 def _forward_impl(self, x, sample):
        x = self.conv1(x,sample)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x,sample)
        x = self.layer2(x,sample)
        x = self.layer3(x,sample)
        x = self.layer4(x,sample)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = F.log_softmax(self.fc(x, sample), dim=1)
        return x

    def forward(self, x, sample=False):
        return self._forward_impl(x,sample)

def sample_elbo(self, input, target, NUM_BATCHES, samples=1):
        outputs = torch.zeros(samples, input.shape[0], 7).to(DEVICE)
        log_priors = torch.zeros(samples).to(DEVICE)
        log_variational_posteriors = torch.zeros(samples).to(DEVICE)
        for i in range(samples):
            outputs[i] = self(input, sample=True)
            log_priors[i] = self.log_prior()
            log_variational_posteriors[i] = self.log_variational_posterior()
        log_prior = log_priors.mean()
        log_variational_posterior = log_variational_posteriors.mean()
        negative_log_likelihood = F.nll_loss(outputs.mean(0), target, size_average=False)
        loss = (log_variational_posterior - log_prior)/NUM_BATCHES + negative_log_likelihood
        return loss, log_prior, log_variational_posterior, negative_log_likelihood

File "bayes_backprop.py", line 141, in train
    loss, log_prior, log_variational_posterior, negative_log_likelihood = net.sample_elbo(data, target,NUM_BATCHES)
  File "/scratch/engn8536/project_data/u6724013/caer/bayes_res.py", line 208, in sample_elbo
    outputs[i] = self(input, sample=True)
  File "/usr/local/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in __call__
    result = self.forward(*input, **kwargs)
  File "/scratch/engn8536/project_data/u6724013/caer/bayes_res.py", line 177, in forward
    return self._forward_impl(x,sample)
  File "/scratch/engn8536/project_data/u6724013/caer/bayes_res.py", line 166, in _forward_impl
    x = self.layer1(x,sample)
  File "/usr/local/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in __call__
    result = self.forward(*input, **kwargs)
TypeError: forward() takes 2 positional arguments but 3 were given