GraphAutoEncoder/graphVAESSW.py [129:151]:
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        out_pc = "" #batch*out_pn*out_channel        
        if b_max_pool:
            out_pc = out_neighbors.max(2)
        else:
            out_pc = out_neighbors.sum(2)
        
        out_pc = out_pc + bias
        
        if is_final_layer==False:
            out_pc = self.relu(out_pc) ##self.relu is defined in the init function
        
        
        if self.residual_rate==0:
            return out_pc
        
        if(in_channel != out_channel):
            in_pc_pad = torch.einsum('oi,bpi->bpo',[self.weight_res.view(out_channel,in_channel), in_pc_pad])

        out_pc_res = []
        if(in_pn == out_pn):
            out_pc_res = in_pc_pad[:,0:in_pn].clone()
        else:
            p_neighbors_raw = self.p_neighbors            
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GraphAutoEncoder/graphVAESSW.py [201:222]:
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        out_pc = ""
        if b_max_pool:
            out_pc = out_neighbors.max(2)
        else:
            out_pc = out_neighbors.sum(2)
        
        out_pc = out_pc + bias
        
        if is_final_layer==False:
            out_pc = self.relu(out_pc)
                
        if self.residual_rate==0:
            return out_pc        
                
        if(in_channel != out_channel):
            in_pc_pad = torch.einsum('oi,bpi->bpo',[self.weight_res.view(out_channel,in_channel), in_pc_pad])

        out_pc_res = []
        if(in_pn == out_pn):
            out_pc_res = in_pc_pad[:,0:in_pn].clone()
        else:
            p_neighbors_raw = self.p_neighbors
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