class_docstr: Plugin for annealing logic in Flair.
class_docstr_tok: ["Plugin","for","annealing","logic","in","Flair","."]
code: def __init__(
      self,
      base_path,
      min_learning_rate,
      anneal_factor,
      patience,
      initial_extra_patience,
      anneal_with_restarts,
  ) -> None:
      super().__init__()
  
      # path to store the model
      self.base_path = base_path
  
      # special annealing modes
      self.anneal_with_restarts = anneal_with_restarts
  
      # determine the min learning rate
      self.min_learning_rate = min_learning_rate
  
      self.anneal_factor = anneal_factor
      self.patience = patience
      self.initial_extra_patience = initial_extra_patience
      self.scheduler: AnnealOnPlateau
  
file_path: flair/flair/trainers/plugins/functional/anneal_on_plateau.py
imports: import logging
  import os
  from typing import Any, Dict
  from flair.trainers.plugins.base import TrainerPlugin, TrainingInterrupt
  from flair.trainers.plugins.metric_records import MetricRecord
  from flair.training_utils import AnnealOnPlateau
label_desc: Use image node_id 1 to create a new AnnealingPlugin object from inherited base classes: TrainerPlugin with example: obj = AnnealingPlugin(base_path, min_learning_rate, anneal_factor, patience, initial_extra_patience, anneal_with_restarts)