class_docstr: Base class for defining distillation loss functions. Sub-classes must implement `_forward_distill` function.
  
  Args:
      opts: command line arguments
docstr: Computes distillation loss.
  
  Args:
      input_sample: Input image tensor
      prediction: Student model's output.
  
  Shapes:
      input_sample: Shape is [Batch size, 3, height, width]
      prediction: Shape is [Batch size, number of classes]
  
  Returns:
      A scalar loss value.
docstr_tok: ["Computes","distillation","loss",".","Args",":","input_sample",":","Input","image","tensor","prediction",":","Student","model","'s","output",".","Shapes",":","input_sample",":","Shape","is","[","Batch","size",",","3",",","height",",","width","]","prediction",":","Shape","is","[","Batch","size",",","number","of","classes","]","Returns",":","A","scalar","loss","value","."]
code: def _forward_distill(
      self, input_sample: Tensor, prediction: Tensor, *args, **kwargs
  ) -> Tensor:
      """Computes distillation loss.
  
      Args:
          input_sample: Input image tensor
          prediction: Student model's output.
  
      Shapes:
          input_sample: Shape is [Batch size, 3, height, width]
          prediction: Shape is [Batch size, number of classes]
  
      Returns:
          A scalar loss value.
      """
      raise NotImplementedError
  
file_path: ml-cvnets/loss_fn/distillation/base_distillation.py
imports: import argparse
  from typing import Mapping, Union
  import torch
  from torch import Tensor
  from loss_fn import LOSS_REGISTRY, BaseCriteria
  from loss_fn.utils.build_helper import build_cls_teacher_from_opts
  from utils import logger
label_desc: Use image node_id 4 for calling the BaseDistillationCriteria obj's underlying member method code with example usage: obj._forward_distill(input_sample, prediction) without return types