datasets/ClassPrioritySampler.py [94:107]:
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        lefti = self.pointer_to_treeidx(0)
        righti = self.pointer_to_treeidx(self.capacity-1)
        self.tree[lefti:righti+1] = total_weights

        # Iteratively find a parent layer
        while lefti != 0 and righti != 0:
            lefti = (lefti - 1) // 2 if lefti != 0 else 0
            righti = (righti - 1) // 2 if righti != 0 else 0
            
            # Assign paraent weights from right to left
            for i in range(righti, lefti-1, -1):
                self.tree[i] = self.tree[2*i+1] + self.tree[2*i+2]
    
    def get_adaptive_weights(self):
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datasets/MixedPrioritizedSampler.py [61:74]:
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        lefti = self.pointer_to_treeidx(0)
        righti = self.pointer_to_treeidx(self.capacity-1)
        self.tree[lefti:righti+1] = total_weights

        # Iteratively find a parent layer
        while lefti != 0 and righti != 0:
            lefti = (lefti - 1) // 2 if lefti != 0 else 0
            righti = (righti - 1) // 2 if righti != 0 else 0
            
            # Assign paraent weights from right to left
            for i in range(righti, lefti-1, -1):
                self.tree[i] = self.tree[2*i+1] + self.tree[2*i+2]
    
    def get_adaptive_weights(self):
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