docstr: Represents any metric.
  
  Parameters
  ----------
  name : str
      A name for the metric.
  lower_is_better : bool
      Indicates if the metric is better for lower values if True,
      otherwise it is assumed that higher values are better.
docstr_tok: ["Represents","any","metric",".","Parameters","--","--","--","--","--","name",":","str","A","name","for","the","metric",".","lower_is_better",":","bool","Indicates","if","the","metric","is","better","for","lower","values","if","True",",","otherwise","it","is","assumed","that","higher","values","are","better","."]
code: def __init__(self, name, lower_is_better=True):
      """
      Represents any metric.
  
      Parameters
      ----------
      name : str
          A name for the metric.
      lower_is_better : bool
          Indicates if the metric is better for lower values if True,
          otherwise it is assumed that higher values are better.
      """
      self.name = name
      self.lower_is_better = lower_is_better
  
file_path: small-text/small_text/integrations/pytorch/model_selection.py
imports: import logging
  from collections import OrderedDict
  from pathlib import Path
  from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
  from small_text.utils.annotations import deprecated
label_desc: Use image node_id 1 to create a new Metric object from inherited base classes: object with example: obj = Metric(name, lower_is_better)