code: def binary_crossentropy(y_true, y_pred):
      """Binary cross-entropy.
  
      Args:
        y_true: tf.Tensor.
          Tensor of 0s and 1s.
        y_pred: tf.Tensor.
          Tensor of real values (logit probabilities), with same shape as
          `y_true`.
      """
      y_true = tf.cast(y_true, tf.float32)
      y_pred = tf.cast(y_pred, tf.float32)
      return tf.reduce_mean(
          tf.nn.sigmoid_cross_entropy_with_logits(
              logits=y_pred, labels=y_true
          )
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import six
  import tensorflow
  from edward.models import RandomVariable
  from edward.util import check_data, get_session, compute_multinomial_mode, with_binary_averaging
label_desc: Use image node_id 5 for calling a global function with example usage: binary_crossentropy(y_true, y_pred) and returns: tf