so, what should i do for the correction.
this my Resnet block
class ResNet(torch.nn.Module):
def init(self, block , num_layers, classes=2):
super(ResNet,self).init()
self.input_planes = 64
self.conv1  = torch.nn.Conv1d(1, 64, kernel_size=3, stride=1, padding=1)
self.bn1    = torch.nn.BatchNorm1d(64)
self.layer1 = self._layer(block, 64, num_layers[0], stride=1)
self.layer2 = self._layer(block, 128, num_layers[1], stride=1)
self.layer3 = self._layer(block, 256, num_layers[2], stride=1)
self.layer4 = self._layer(block, 512, num_layers[3], stride=1)
self.averagePool = torch.nn.AvgPool1d(kernel_size = 4, stride = 1)
self.fc     = torch.nn.Linear(512*block.expension, classes)
def _layer(self, block, planes, num_layers, stride= 1):
    dim_change= None
    if stride != 1 or planes != self.input_planes*block.expension:
        dim_change = torch.nn.Sequential(torch.nn.Conv1d(self.input_planes, planes*block.expension, kernel_size=1, stride=stride),
                                        torch.nn.BatchNorm1d(planes*block.expension))
    netLayers = []
    netLayers.append(block(self.input_planes,planes, stride=stride, dim_change= dim_change))
    self.input_planes = planes * block.expension
    for i in range(1,num_layers):
        netLayers.append(block(self.input_planes,planes))
        self.input_planes = planes* block.expension
        
    return torch.nn.Sequential(*netLayers)