code: def test_convergence_with_soft_alpha_only(
      self, loss_type: str, temperature: float
  ):
      criterion = DistillationLoss.from_config(
          AttrDict(
              {
                  "soft_target_alpha": 1.0,
                  "temperature": temperature,
                  "loss_type": loss_type,
              }
          )
      )
  
      # An input and its soft target
      x = nn.Parameter(torch.tensor([[1.0, 2.0, 3.0]]))
      y = torch.tensor([[3.0, 2.0, 1.0]])
      criterion.teacher_logits = y
      t = torch.tensor([0], dtype=torch.int64)
  
      # Optimizing the input to imitate soft target
      optimizer = optim.SGD([x], lr=1.0)
      for _ in range(100):
          loss = criterion(x, t)
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
      # Assert convergence
      self.assertTrue(torch.allclose(x.data, y.data))
  
imports: import logging
  import unittest
  from collections import namedtuple
  import torch
  import torch.nn
  import torch.optim
  from classy_vision.generic.distributed_util import set_cpu_device
  from parameterized import param, parameterized
  from vissl.config.attr_dict import AttrDict
  from vissl.losses.barlow_twins_loss import BarlowTwinsCriterion
  from vissl.losses.cross_entropy_multiple_output_single_target import CrossEntropyMultipleOutputSingleTargetCriterion, CrossEntropyMultipleOutputSingleTargetLoss
  from vissl.losses.distillation_loss import DistillationCriterion, DistillationLoss
  from vissl.losses.multicrop_simclr_info_nce_loss import MultiCropSimclrInfoNCECriterion
  from vissl.losses.simclr_info_nce_loss import SimclrInfoNCECriterion
  from vissl.losses.swav_loss import SwAVCriterion
label_desc: Use image node_id 3 for calling the TestDistillationCriterion obj's underlying member method code with example usage: obj.test_convergence_with_soft_alpha_only(loss_type, temperature) without return types