code: def main(argv):
      del argv
      if tf.io.gfile.exists(FLAGS.model_dir):
          print(
              "Warning: deleting old log directory at {}".format(
                  FLAGS.model_dir
              )
          )
          tf.io.gfile.rmtree(FLAGS.model_dir)
      tf.io.gfile.makedirs(FLAGS.model_dir)
  
      # Generate a toy classification dataset.
      w_true, b_true, features, labels = toy_logistic_data(
          FLAGS.num_examples
      )
      toy_logistic_sequence = ToyDataSequence(
          features, labels, FLAGS.batch_size
      )
  
      # Define and train a bayesian logistic regression model.
      model = create_model(FLAGS.num_examples, NUM_DIMENSIONS)
      model.fit(
          toy_logistic_sequence, epochs=FLAGS.num_epochs, shuffle=True
      )
      # Visualize some draws from the weights posterior.
      candidate_w_bs = [
          (
              model.layers[-1].kernel_posterior.sample().numpy(),
              model.layers[-1].bias_posterior.sample().numpy(),
          )
          for _ in range(FLAGS.num_monte_carlo)
      ]
      visualize_decision(
          features,
          labels,
          (w_true, b_true),
          candidate_w_bs,
          fname=os.path.join(FLAGS.model_dir, "weights_inferred.png"),
      )
  
file_path: probability/tensorflow_probability/examples/logistic_regression.py
imports: import os
  from absl import app
  from absl import flags
  from matplotlib import cm
  from matplotlib import figure
  from matplotlib.backends import backend_agg
  import numpy
  import tensorflow.compat.v2
  import tensorflow_probability
  from tensorflow_probability.python.internal import tf_keras
label_desc: Use image node_id 4 for calling a global function with example usage: main(argv) without return types