code: def generate_model_outputs_cases() -> (
      Iterable[Dict[str, Union[float, torch.Tensor]]]
  ):
      return [
          # random_inputs
          {
              "labels": torch.tensor([[1, 0, 0, 1, 1]]),
              "predictions": torch.tensor([[0.2, 0.6, 0.8, 0.4, 0.9]]),
              "weights": torch.tensor([[0.3, 0.2, 0.5, 0.8, 0.7]]),
              "threshold": 0.6,
              "expected_accuracy": torch.tensor([0.28]),
          },
          # perfect_condition
          {
              "labels": torch.tensor([[1, 0, 0, 1, 1]]),
              "predictions": torch.tensor([[1, 0, 0, 1, 1]]),
              "weights": torch.tensor([[1] * 5]),
              "threshold": 0.6,
              "expected_accuracy": torch.tensor([1.0]),
          },
          # inverse_prediction
          {
              "labels": torch.tensor([[1, 0, 0, 1, 1]]),
              "predictions": torch.tensor([[0, 1, 1, 0, 0]]),
              "weights": torch.tensor([[1] * 5]),
              "threshold": 0.1,
              "expected_accuracy": torch.tensor([0.0]),
          },
      ]
  
imports: import unittest
  from typing import Dict, Iterable, Type, Union
  import torch
  from torch import no_grad
  from torchrec.metrics.accuracy import AccuracyMetric, compute_accuracy
  from torchrec.metrics.metrics_config import DefaultTaskInfo
  from torchrec.metrics.rec_metric import RecComputeMode, RecMetric
  from torchrec.metrics.test_utils import metric_test_helper, rec_metric_value_test_launcher, RecTaskInfo, TestMetric
label_desc: Use image node_id 1 for calling a global function with example usage: generate_model_outputs_cases() and returns: list