[Fix] Fix a vital performance bug when run in graph mode deepray will…

[fuhailin]

broadcast at the beginning of every execution

Description

Broadcast too many times

Brief Description of the PR:

Fixes # (issue)

Type of change

• Bug fix
• New Tutorial
• Updated or additional documentation
• Additional Testing
• New Activation and the changes conform to the activation contribution guidelines
• New Callback and the changes conform to the callback contribution guidelines
• New Image addition and the changes conform to the image op contribution guidelines
• New Layer and the changes conform to the layer contribution guidelines
• New Loss and the changes conform to the loss contribution guidelines
• New Metric and the changes conform to the metric contribution guidelines
• New Optimizer and the changes conform to the optimizer contribution guidelines
• New RNN Cell and the changes conform to the rnn contribution guidelines
• New Seq2seq addition and the changes conform to the seq2seq contribution guidelines
• New Text addition and the changes conform to the text op contribution guidelines

Checklist:

• I've properly formatted my code according to the guidelines
  • By running Black + Flake8
  • By running pre-commit hooks
• This PR addresses an already submitted issue for Deepray
• I have made corresponding changes to the documentation
• I have added tests that prove my fix is effective or that my feature works
• This PR contains modifications to C++ custom-ops

How Has This Been Tested?

If you're adding a bugfix or new feature please describe the tests that you ran to verify your changes:
*

[MoFHeka]

Creating variable first_batch under init_scope would be better? Which makes sure this variable was created in eager mode to be compatible with python logical controlling code in any time, and also guarantees a safety from TF tape and other contexts.

with tf.init_scope():
    # Initialization runs with eager execution enabled
    # assert tf.executing_eagerly()
    tf.Variable(True, trainable=False, dtype=tf.bool, name='first_batch')

[fuhailin]

Done.