Don’t mind.  Not sure how to correctly format posting code here, hope this is right.  I started with the pytorch wide-resnet code since I will be doing stuff later that requires putting it back that way.  https://github.com/meliketoy/wide-resnet.pytorch 9. Flipping bias from True to False makes it work/not work repeatably.
modified networks/wide_resnet.py:
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.autograd import Variable
import math
import sys
import numpy as np
import pdb
def conv_init_pr(m):
    for m in m.modules():
        if isinstance(m, nn.Conv2d):
            n = m.kernel_size[0]*m.kernel_size[1]*m.out_channels
            m.weight.data.normal_(0, math.sqrt(2./n))
class wide_basic(nn.Module):
    def __init__(self, in_planes, planes, dropout_rate, stride=1, kernel_size=3):
        super(wide_basic, self).__init__()
        self.conv2 = nn.Conv2d(in_planes, planes, kernel_size=kernel_size, stride=stride, padding=1, bias=True)
    def forward(self, x):
        out = self.conv2(F.relu(x))
        return out
class Wide_ResNet(nn.Module):
    def __init__(self, depth, widen_factor, dropout_rate, num_classes):
        super(Wide_ResNet, self).__init__()
        self.in_planes = 3
        n = 1
        k = 1
        self.layer1 = self._wide_layer(wide_basic, 16, n, dropout_rate, stride=2, kernel_size=3)
        self.layer2 = self._wide_layer(wide_basic, 10, n, dropout_rate, stride=2, kernel_size=16)
    def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride, kernel_size):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, dropout_rate, stride, kernel_size))
            self.in_planes = planes
        return nn.Sequential(*layers)
    def forward(self, x):
        out2 = self.layer1(x)
        out3 = self.layer2(out2)
        out4 = F.relu(out3)
        return out4.squeeze()
if __name__ == '__main__':
    net=Wide_ResNet(28, 10, 0.3, 10)
    y = net(Variable(torch.randn(1,3,32,32)))

    print(y.size())

...