class_docstr: This loss allows to distill a SwAV model
class_docstr_tok: ["This","loss","allows","to","distill","a","SwAV","model"]
code: def forward_to_teacher_crop(
      self,
      scores: torch.Tensor,
      teacher_scores: torch.Tensor,
      batch_size: int,
      student_crop_ids: List[int],
  ):
      # Compute the target assignments, taking crop_id as the features
      # used to compute the codes to which other crops will be mapped
      with torch.no_grad():
          assignments = self.compute_teacher_assignements(
              teacher_scores
          )
  
      # For each crop other than the one used as target assignment (id 0)
      # compute the cross entropy between the target assigment and the
      # softmax of the crop scores
      loss = 0
      for p in student_crop_ids:
          crop_scores = scores[batch_size * p : batch_size * (p + 1)]
          if self.use_double_prec:
              crop_scores = crop_scores.double() / np.float64(
                  self.temperature
              )
          else:
              crop_scores = crop_scores / self.temperature
          minus_cross_entropies = torch.sum(
              assignments * self.log_softmax(crop_scores),
              dim=1,
              dtype=assignments.dtype,
          )
          loss -= torch.mean(minus_cross_entropies)
  
      # Average of the contribution of each crop (we don't want and
      # increase in the number of crop to impact the loss magnitude
      # and force us to update the LR)
      loss /= len(student_crop_ids)
  
      # Stop training if NaN appears and log the output to help debugging
      if torch.isnan(loss):
          logging.info(
              f"Infinite Loss or NaN. Loss value: {loss}, rank: {self.dist_rank}"
          )
          scores_output_file = os.path.join(
              self.output_dir,
              "rank" + str(self.dist_rank) + "_scores.pth",
          )
          assignments_out_file = os.path.join(
              self.output_dir,
              "rank" + str(self.dist_rank) + "_assignments.pth",
          )
          with PathManager.open(scores_output_file, "wb") as fwrite:
              torch.save(scores, fwrite)
          with PathManager.open(assignments_out_file, "wb") as fwrite:
              torch.save(assignments, fwrite)
          logging.info(
              f"Saved the scores matrix to: {scores_output_file}"
          )
          logging.info(
              f"Saved the assignment matrix to: {assignments_out_file}"
          )
  
      return loss
  
imports: import logging
  import os
  import pprint
  from typing import List, Optional, Union
  import numpy
  import torch
  import torch.nn.functional
  from classy_vision.generic.distributed_util import all_reduce_max, get_cuda_device_index, get_rank, get_world_size
  from classy_vision.losses import ClassyLoss, register_loss
  from fvcore.common.file_io import PathManager
  from torch import nn
  from vissl.config import AttrDict
  from vissl.losses.distibuted_sinkhornknopp import distributed_sinkhornknopp
label_desc: Use image node_id 5 for calling the SwAVDistillationCriterion obj's underlying member method code with example usage: obj.forward_to_teacher_crop(scores, teacher_scores, batch_size, student_crop_ids) and returns: loss