code: def test_avg_non_zero_reducer(self):
      reducer = AvgNonZeroReducer()
      for dtype in TEST_DTYPES:
          batch_size = 100
          embedding_size = 64
          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)
          zero_losses = (
              torch.zeros(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"),
          ]:
              for L in [losses, zero_losses]:
                  loss_dict = {
                      "loss": {
                          "losses": L,
                          "indices": indices,
                          "reduction_type": reduction_type,
                      }
                  }
                  output = reducer(loss_dict, embeddings, labels)
                  filtered_L = L[L > 0]
                  if len(filtered_L) > 0:
                      correct_output = torch.mean(filtered_L)
                  else:
                      correct_output = torch.mean(L) * 0
                  self.assertTrue(output == correct_output)
  
imports: import unittest
  import torch
  from pytorch_metric_learning.reducers import AvgNonZeroReducer
  from ..None import TEST_DEVICE, TEST_DTYPES
label_desc: Use image node_id 1 for calling the TestAvgNonZeroReducer obj's underlying member method code with example usage: obj.test_avg_non_zero_reducer() without return types