hype_kg/codes/dataloader.py [57:69]:
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        return positive_sample, negative_sample, subsampling_weight, self.mode
    
    @staticmethod
    def collate_fn(data):
        '''Stacks positive_samples, negative_samples and their weights into one tensor.'''
        positive_sample = torch.stack([_[0] for _ in data], dim=0)
        negative_sample = torch.stack([_[1] for _ in data], dim=0)
        subsample_weight = torch.cat([_[2] for _ in data], dim=0)
        mode = data[0][3]
        return positive_sample, negative_sample, subsample_weight, mode
    
    @staticmethod
    def count_frequency(triples, true_tail, start=4):
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hype_kg/codes/dataloader.py [128:140]:
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        return positive_sample, negative_sample, subsampling_weight, self.mode
    
    @staticmethod
    def collate_fn(data):
        '''Stacks positive_samples, negative_samples and their weights into one tensor.'''
        positive_sample = torch.stack([_[0] for _ in data], dim=0)
        negative_sample = torch.stack([_[1] for _ in data], dim=0)
        subsample_weight = torch.cat([_[2] for _ in data], dim=0)
        mode = data[0][3]
        return positive_sample, negative_sample, subsample_weight, mode
    
    @staticmethod
    def count_frequency(triples, true_tail, start=4):
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