def forward(self, model, sample, reduce=True):
        """Compute the loss for the given sample.

        Returns a tuple with three elements:
        1) the loss
        2) the sample size, which is used as the denominator for the gradient
        3) logging outputs to display while training
        """
        model.eval()
        all_retrieval_result = {}
        random.seed(42)
        for context_length in range(self.cfg.interval, self.cfg.tokens_per_sample + 1, self.cfg.interval):
            all_length = (context_length - 150)
            local_retrieval_result = []
            for depth_ratio in range(1, 11):
                prefix_length = int(all_length * depth_ratio / 11)
                suffix_length = all_length - prefix_length
                n_correct = 0
                for _ in range(5):
                    if self.cfg.needle_num > 1:
                        first_needle_num = random.randint(1, self.cfg.needle_num - 1)
                        second_needle_num = self.cfg.needle_num + 1 - first_needle_num
                        first_length_list = random_partition(prefix_length, first_needle_num)
                        second_length_list = random_partition(suffix_length, second_needle_num)
                        final_length = second_length_list.pop()
                    else:
                        first_length_list = [prefix_length]
                        second_length_list = []
                        final_length = suffix_length
                    prompt, pass_key = self.generate_prompt_landmark(first_length_list, second_length_list, final_length)
                    prompt_tokens = self.task.tokenizer.encode(prompt, bos=True)
                    prompt_tokens = torch.tensor([prompt_tokens], device="cuda")
                    print(prompt_tokens.shape)
                    output = self.generate(model, prompt_tokens)
                    pred = self.task.tokenizer.decode(output[0, prompt_tokens.shape[1]:])
                    print("Answer: ", pass_key)
                    print("Pred: ", pred)
                    if pass_key in pred:
                        n_correct += 1
                local_retrieval_result.append(n_correct / 5)
            all_retrieval_result[context_length] = local_retrieval_result

        print(all_retrieval_result)
        return 0, 1, {"loss": 0}
Compute the loss for the given sample.

        Returns a tuple with three elements:
        1) the loss
        2) the sample size, which is used as the denominator for the gradient
        3) logging outputs to display while training
        
YOCO/yoco/criterions/multi_needle.py