docstr: A helper method that records a forward metric
  for the given key. A user should call this before
  their neural network forward.
  Args:
      key (str): unique id for metric within a group
      cuda (bool): indicator to determine if this is a CUDA metric
docstr_tok: ["A","helper","method","that","records","a","forward","metric","for","the","given","key",".","A","user","should","call","this","before","their","neural","network","forward",".","Args",":","key","(","str",")",":","unique","id","for","metric","within","a","group","cuda","(","bool",")",":","indicator","to","determine","if","this","is","a","CUDA","metric"]
code: def record_forward_start(self, key, cuda=True):
      r"""
      A helper method that records a forward metric
      for the given key. A user should call this before
      their neural network forward.
      Args:
          key (str): unique id for metric within a group
          cuda (bool): indicator to determine if this is a CUDA metric
      """
      self.__metrics_logger.record_start(
          self.FORWARD_METRIC, key, self.FORWARD_PASS, cuda
      )
  
imports: import functools
  import time
  from abc import ABC, abstractmethod
  import torch
  from metrics.MetricsLogger import MetricsLogger
label_desc: Use image node_id 7 for calling the TrainerBase obj's underlying member method code with example usage: obj.record_forward_start(key, cuda) without return types