code: def test_pit_process(etype, dtype, num_spkrs, m_str, data_idx):
      bs = 10
      m = importlib.import_module(m_str)
  
      losses_2 = torch.ones([bs, 4], dtype=torch.float32)
      for i in range(bs):
          losses_2[i][i % 4] = 0
      true_losses_2 = torch.ones(bs, dtype=torch.float32) / 2
      perm_choices_2 = [[0, 1], [1, 0], [1, 0], [0, 1]]
      true_perm_2 = []
      for i in range(bs):
          true_perm_2.append(perm_choices_2[i % 4])
      true_perm_2 = torch.tensor(true_perm_2).long()
  
      losses = [losses_2]
      true_losses = [torch.mean(true_losses_2)]
      true_perm = [true_perm_2]
  
      args = make_arg(etype=etype, num_spkrs=num_spkrs)
      model = m.E2E(40, 5, args)
      min_loss, min_perm = model.pit.pit_process(losses[data_idx])
  
      assert min_loss == true_losses[data_idx]
      assert torch.equal(min_perm, true_perm[data_idx])
  
file_path: espnet/test/test_multi_spkrs.py
imports: import argparse
  import importlib
  import re
  import numpy
  import pytest
  import torch
label_desc: Use image node_id 5 for calling a global function with example usage: test_pit_process(etype, dtype, num_spkrs, m_str, data_idx) without return types