class_docstr: Base class for defining loss functions. Sub-classes must implement compute_loss function.
  
  Args:
      opts: command line arguments
class_docstr_tok: ["Base","class","for","defining","loss","functions",".","Sub-classes","must","implement","compute_loss","function",".","Args",":","opts",":","command","line","arguments"]
docstr: Compute the loss.
  
  Args:
      input_sample: Input to the model.
      prediction: Model's output
      target: Ground truth labels
docstr_tok: ["Compute","the","loss",".","Args",":","input_sample",":","Input","to","the","model",".","prediction",":","Model","'s","output","target",":","Ground","truth","labels"]
code: def forward(
      self,
      input_sample: Any,
      prediction: Any,
      target: Any,
      *args,
      **kwargs,
  ) -> Any:
      """Compute the loss.
  
      Args:
          input_sample: Input to the model.
          prediction: Model's output
          target: Ground truth labels
      """
      raise NotImplementedError
  
file_path: ml-cvnets/loss_fn/base_criteria.py
imports: import abc
  import argparse
  from typing import Any
  from torch import nn
  from utils import logger
label_desc: Use image node_id 3 for calling the BaseCriteria obj's underlying member method code with example usage: obj.forward(input_sample, prediction, target) without return types