import tensorflow as tf

from ..flags import FLAGS
from ..variable import variable



def embeddings(*, id_space_size, embedding_size, name=None):
  return variable([id_space_size, embedding_size], name=name)


def word_embeddings(name="word_embeddings"):
  if FLAGS.word_embeddings is None:
    return embeddings(id_space_size=FLAGS.word_space_size,
                      embedding_size=FLAGS.word_embedding_size,
                      name=name)
  return tf.Variable(tf.cast(FLAGS.word_embeddings, FLAGS.float_type),
                     name=name)

import tensorflow as tf

from ..flags import FLAGS
from ..variable import variable
from ..util import func_scope



@func_scope()
def embeddings(*, id_space_size, embedding_size, name=None):
  return variable([id_space_size, embedding_size], name=name)


@func_scope()
def word_embeddings(name="word_embeddings"):
  if FLAGS.word_embeddings is None:
    return embeddings(id_space_size=FLAGS.word_space_size,
                      embedding_size=FLAGS.word_embedding_size,
                      name=name)
  return tf.Variable(tf.cast(FLAGS.word_embeddings, FLAGS.float_type),
                     name=name)

Add func_scope to embedding functions
Add func_scope to embedding functions

  import tensorflow as tf
  
  from ..flags import FLAGS
  from ..variable import variable
+ from ..util import func_scope
  
  
  
+ @func_scope()
  def embeddings(*, id_space_size, embedding_size, name=None):
    return variable([id_space_size, embedding_size], name=name)
  
  
+ @func_scope()
  def word_embeddings(name="word_embeddings"):
    if FLAGS.word_embeddings is None:
      return embeddings(id_space_size=FLAGS.word_space_size,
                        embedding_size=FLAGS.word_embedding_size,
                        name=name)
    return tf.Variable(tf.cast(FLAGS.word_embeddings, FLAGS.float_type),
                       name=name)
  
Add func_scope to embedding functions
## Code Before:
import tensorflow as tf

from ..flags import FLAGS
from ..variable import variable



def embeddings(*, id_space_size, embedding_size, name=None):
  return variable([id_space_size, embedding_size], name=name)


def word_embeddings(name="word_embeddings"):
  if FLAGS.word_embeddings is None:
    return embeddings(id_space_size=FLAGS.word_space_size,
                      embedding_size=FLAGS.word_embedding_size,
                      name=name)
  return tf.Variable(tf.cast(FLAGS.word_embeddings, FLAGS.float_type),
                     name=name)

## Instruction:
Add func_scope to embedding functions
## Code After:
import tensorflow as tf

from ..flags import FLAGS
from ..variable import variable
from ..util import func_scope



@func_scope()
def embeddings(*, id_space_size, embedding_size, name=None):
  return variable([id_space_size, embedding_size], name=name)


@func_scope()
def word_embeddings(name="word_embeddings"):
  if FLAGS.word_embeddings is None:
    return embeddings(id_space_size=FLAGS.word_space_size,
                      embedding_size=FLAGS.word_embedding_size,
                      name=name)
  return tf.Variable(tf.cast(FLAGS.word_embeddings, FLAGS.float_type),
                     name=name)
