code: def test_mean_reducer(self):
      reducer = MeanReducer()
      sum_reducer = SumReducer()
      batch_size = 100
      embedding_size = 64
      for dtype in TEST_DTYPES:
          embeddings = (
              torch.randn(batch_size, embedding_size)
              .type(dtype)
              .to(TEST_DEVICE)
          )
          labels = torch.randint(0, 10, (batch_size,))
          pair_indices = (
              torch.randint(0, batch_size, (batch_size,)),
              torch.randint(0, batch_size, (batch_size,)),
          )
          triplet_indices = pair_indices + (
              torch.randint(0, batch_size, (batch_size,)),
          )
          losses = torch.randn(batch_size).type(dtype).to(TEST_DEVICE)
  
          for indices, reduction_type in [
              (torch.arange(batch_size), "element"),
              (pair_indices, "pos_pair"),
              (pair_indices, "neg_pair"),
              (triplet_indices, "triplet"),
          ]:
              loss_dict = {
                  "loss": {
                      "losses": losses,
                      "indices": indices,
                      "reduction_type": reduction_type,
                  }
              }
              output = reducer(loss_dict, embeddings, labels)
              correct_output = torch.mean(losses)
              self.assertTrue(output == correct_output)
  
              output = sum_reducer(loss_dict, embeddings, labels)
              correct_output = torch.sum(losses)
              self.assertTrue(output == correct_output)
  
imports: import unittest
  import torch
  from pytorch_metric_learning.reducers import MeanReducer, SumReducer
  from ..None import TEST_DEVICE, TEST_DTYPES
label_desc: Use image node_id 1 for calling the TestMeanReducer obj's underlying member method code with example usage: obj.test_mean_reducer() without return types