class_docstr: This class is used for updating the value of β during the annealing process.
  When β reaches the value of anneal_cap, it stops increasing.
docstr: At the end of each batch the beta should is updated until it reaches the values of anneal cap.
code: def on_batch_end(self, epoch, logs={}):
      """At the end of each batch the beta should is updated until it reaches the values of anneal cap."""
      self.update_count = self.update_count + 1
  
      new_beta = min(
          1.0 * self.update_count / self.total_anneal_steps,
          self.anneal_cap,
      )
  
      K.set_value(self.beta, new_beta)
  
imports: import numpy
  import matplotlib.pyplot
  import seaborn
  import tensorflow
  from tensorflow.keras.layers import *
  from tensorflow.keras.models import Model
  from tensorflow.keras.losses import binary_crossentropy
  from tensorflow.keras import backend
  from tensorflow.keras.callbacks import ReduceLROnPlateau, Callback
  from recommenders.evaluation.python_evaluation import ndcg_at_k
label_desc: Use image node_id 3 for calling the AnnealingCallback obj's underlying member method code with example usage: obj.on_batch_end(epoch, logs) without return types