code: def test_with_correlated_embedding(self):
      for n_samples in [1, 2, 8, 16]:
          for memory_bank_size in [0, 1, 2, 8, 15, 16, 17]:
              for temperature in [0.1, 1, 7]:
                  for gather_distributed in [False, True]:
                      out0 = np.random.random((n_samples, 1))
                      out1 = np.random.random((n_samples, 1))
                      out0 = np.concatenate([out0, 2 * out0], axis=1)
                      out1 = np.concatenate([out1, 2 * out1], axis=1)
                      out0 = torch.FloatTensor(out0)
                      out1 = torch.FloatTensor(out1)
                      out0.requires_grad = True
  
                      with self.subTest(
                          msg=(
                              f"n_samples: {n_samples}, memory_bank_size: {memory_bank_size},"
                              f"temperature: {temperature}, gather_distributed: {gather_distributed}"
                          )
                      ):
                          loss_function = NTXentLoss(
                              temperature=temperature,
                              memory_bank_size=memory_bank_size,
                          )
                          if memory_bank_size > 0:
                              for i in range(
                                  int(memory_bank_size / n_samples) + 2
                              ):
                                  # fill the memory bank over multiple rounds
                                  loss = float(
                                      loss_function(out0, out1)
                                  )
                              expected_loss = -1 * np.log(
                                  1 / (memory_bank_size + 1)
                              )
                          else:
                              loss = float(loss_function(out0, out1))
                              expected_loss = -1 * np.log(
                                  1 / (2 * n_samples - 1)
                              )
                          self.assertAlmostEqual(
                              loss, expected_loss, places=5
                          )
  
imports: import unittest
  import numpy
  import pytest
  import torch
  from pytest_mock import MockerFixture
  from torch import distributed
  from lightly.loss import NTXentLoss
label_desc: Use image node_id 3 for calling the TestNTXentLossUnitTest obj's underlying member method code with example usage: obj.test_with_correlated_embedding() without return types