class_docstr: Abstract base class used to build new callbacks. Extend this class to build your own callbacks and overwrite functions
  that you want to monitor. Functions will be called automatically from the trainer once per relevant training event
  (e.g. at the beginning of epoch, end of epoch, beginning of batch, end of batch etc.)
class_docstr_tok: ["Abstract","base","class","used","to","build","new","callbacks",".","Extend","this","class","to","build","your","own","callbacks","and","overwrite","functions","that","you","want","to","monitor",".","Functions","will","be","called","automatically","from","the","trainer","once","per","relevant","training","event","(","e.g",".","at","the","beginning","of","epoch",",","end","of","epoch",",","beginning","of","batch",",","end","of","batch","etc",".",")"]
code: def __init__(self):
      self.params = None
      self.trainer = None
  
file_path: pywick/pywick/callbacks/Callback.py
imports: from typing import Collection
label_desc: Use image node_id 1 to create a new Callback object  with example: obj = Callback()