class_docstr: The source code of this class is under the MIT License and was copied from the Keras project,
  and has been modified.
  
  Stop training when a monitored quantity has stopped improving.
  
  Args:
      monitor (str): Quantity to be monitored.
      min_delta (float): Minimum change in the monitored quantity to qualify as an improvement,
          i.e. an absolute change of less than min_delta, will count as no improvement.
          (Default value = 0)
      patience (int): Number of epochs with no improvement after which training will be stopped.
          (Default value = 0)
      verbose (bool): Whether to print when early stopping is done.
          (Default value = False)
      mode (str): One of {'min', 'max'}. In `min` mode, training will stop when the quantity
          monitored has stopped decreasing; in `max` mode it will stop when the quantity monitored has
          stopped increasing.
          (Default value = 'min')
code: def on_train_begin(self, logs: Dict):
      # Allow instances to be re-used
      self.wait = 0
      self.stopped_epoch = 0
      self.best = np.Inf if self.mode == "min" else -np.Inf
  
imports: from typing import Dict
  import numpy
  from poutyne.framework.callbacks.callbacks import Callback
label_desc: Use image node_id 2 for calling the EarlyStopping obj's underlying member method code with example usage: obj.on_train_begin(logs) without return types