How to custom pretrained model to concat scalar data in FC layer

I have X-ray images and clinical data trying to predict regression problem.
How to custom fast.ai library’s pretrained model able to inject scalar tensor vector after in FC layer after Flatten()?

I’ve modified Resnet34, by guiding from

here http://forums.fast.ai/t/custom-pretrained-model-with-fastai/7896

class ConvnetBuilderRes34():
"""Class representing a convolutional network.
Arguments:
    c (int): size of the last layer
    is_multi (bool): is multilabel classification
    is_reg (bool): is a regression
    ps (float): dropout parameter for last layer
"""
def __init__(self,c,is_multi,is_reg,ps=None):
    self.c,self.is_multi,self.is_reg = c,is_multi,is_reg
    self.ps = ps or 0.5
    self.lr_cut = 8
    res = resnet34(True)
    res.conv1 = nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    layers = children(res)[0:8]
    layers += [Flatten()]     
    self.top_model = nn.Sequential(*layers)
    
    fc_layers = self.create_fc_layer(6144, 1, p=None)
    self.n_fc = len(fc_layers)
    self.fc_model = to_gpu(nn.Sequential(*fc_layers))
    apply_init(self.fc_model, kaiming_normal)
    self.model = nn.Sequential(*(layers+fc_layers))           

def create_fc_layer(self, ni, nf, p, actn=None):
    res=[]
    if p: res.append(nn.Dropout(p=p))
    res.append(nn.Linear(in_features=ni, out_features=nf))
    if actn: res.append(actn())
    return res

@property
def name(self): return "Resnet34"

def get_layer_groups(self, do_fc=False):
    if do_fc: 
        m,idxs = self.fc_model,[]
    else:     
        m,idxs = self.model,[self.lr_cut,-self.n_fc]
    lgs = list(split_by_idxs(children(m),idxs))
    return lgs

and

class ConvLearnerRes34(ConvLearner):

@classmethod
def pretrained(cls, data, ps=None, **kwargs):
    model = ConvnetBuilderRes34(data.c, data.is_multi, data.is_reg)
    return cls(data,model,**kwargs)

# redefining freeze to freeze everything but last layer
def freeze(self):
    layers = children(self.model)
    n = len(layers)
    for l in layers:
        l.trainable=False
        for p in l.parameters(): p.requires_grad=False
    l = layers[n-1]
    l.trainable=True
    for p in l.parameters(): p.requires_grad=True
        
def unfreeze_prev_layer(self):
    layers = children(self.model)
    l = layers[9]
    l.trainable=True
    for p in l.parameters(): p.requires_grad=True

Now I’m able to train 1channel Gray X-ray image and output as regression. If I would like to add a clinical data, such as age, sex,… etc after Flatten(), how can I do?

Do I need to create another Pytorch module? Where to feed it?