code: def test_constant_memory_on_repeat_init():
      """Test that when initializing a metric multiple times the memory does not increase.
  
      This only works for metrics with `compute_with_cache=False` as otherwise the cache will keep a reference that python
      gc will not be able to collect and clean.
  
      """
  
      def mem():
          return torch.cuda.memory_allocated() / 1024**2
  
      x = torch.randn(10000).cuda()
  
      for i in range(100):
          m = DummyListMetric(compute_with_cache=False).cuda()
          m(x)
          if i == 0:
              after_one_iter = mem()
  
          # allow for 5% flucturation due to measuring
          assert (
              after_one_iter * 1.05 >= mem()
          ), "memory increased too much above base level"
  
imports: import os
  import pickle
  from collections import OrderedDict
  from typing import Any
  from unittest.mock import Mock
  import cloudpickle
  import numpy
  import psutil
  import pytest
  import torch
  from torch import Tensor, tensor
  from torch.nn import Module, Parameter
  from torchmetrics.classification import BinaryAccuracy
  from torchmetrics.regression import PearsonCorrCoef
  from unittests.helpers import seed_all
  from unittests.helpers.testers import DummyListMetric, DummyMetric, DummyMetricMultiOutput, DummyMetricSum
  from unittests.helpers.utilities import no_warning_call
label_desc: Use image node_id 24 for calling a global function with example usage: test_constant_memory_on_repeat_init() and returns: unknown