class_docstr: Main logger - the aim of this class is to store every log from training
  Log is a float value with corresponding training engine step
class_docstr_tok: ["Main","logger","-","the","aim","of","this","class","is","to","store","every","log","from","training","Log","is","a","float","value","with","corresponding","training","engine","step"]
docstr: Args:
      name: metrics name, e.g. 'val_acc' or 'loss'
      full: flag, if True return all, otherwise as specified by the from_step parameter
  
  Returns:
      a list of log items
docstr_tok: ["Args",":","name",":","metrics","name",",","e.g",".","'val_acc","'","or","'loss'","full",":","flag",",","if","True","return","all",",","otherwise","as","specified","by","the","from_step","parameter","Returns",":","a","list","of","log","items"]
code: def history_shorter(
      self, name: str, full: bool = False
  ) -> List[LogItem]:
      """
      Args:
          name: metrics name, e.g. 'val_acc' or 'loss'
          full: flag, if True return all, otherwise as specified by the from_step parameter
  
      Returns:
          a list of log items
      """
      log_metrics = self.log_history[name]
      if full or self.from_step == 0:
          return log_metrics
      elif self.from_step > 0:
          return [x for x in log_metrics if x.step >= self.from_step]
      else:
          current_from_step = self.current_step + self.from_step
          return [x for x in log_metrics if x.step >= current_from_step]
  
file_path: livelossplot/livelossplot/main_logger.py
imports: import re
  from collections import OrderedDict, defaultdict
  from typing import NamedTuple, Dict, Iterable, List, Pattern, Tuple, Optional, Union
label_desc: Use image node_id 6 for calling the MainLogger obj's underlying member method code with example usage: obj.history_shorter(name, full) and returns: log_metrics, unknown, unknown