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import tensorflow as tf

tf.variable_scope("A") # This is scope "A"

tf.variable_scope(None, "A") # incremented scope "A_1"

reuse= tf.AUTO_REUSE

with tf.variable_scope("A", reuse=reuse):

    with tf.variable_scope("B", reuse=tf.AUTO_REUSE):

        print tf.get_variable("x", (), tf.float32) # 'A/B/x:0'        

    with tf.variable_scope(None, "B"): # Increment B, as expected

        print tf.get_variable("x", (), tf.float32) # 'A/B_1/x:0'

# Re-enter A and try to increment B

with tf.variable_scope("A", reuse=reuse):    

    with tf.variable_scope(None, "B"): # Does not increment B !!!

        print tf.get_variable("x", (), tf.float32) # 'A/B/x:0' !!!

import tensorflow as tf

tf.variable_scope("A") # This is scope "A"

tf.variable_scope(None, "A") # incremented scope "A_1"

reuse= tf.AUTO_REUSE

with tf.variable_scope("A", reuse=reuse):

    with tf.variable_scope("B", reuse=tf.AUTO_REUSE):

        print tf.get_variable("x", (), tf.float32) # 'A/B/x:0'        

    with tf.variable_scope(None, "B"): # Increment B, as expected

        print tf.get_variable("x", (), tf.float32) # 'A/B_1/x:0'

# Re-enter A and try to increment B

with tf.variable_scope("A", reuse=reuse):    

    with tf.variable_scope(None, "B"): # Does not increment B !!!

        print tf.get_variable("x", (), tf.float32) # 'A/B/x:0' !!!

import tensorflow as tf

tf.variable_scope("A") # This is scope "A"

tf.variable_scope(None, "A") # incremented scope "A_1"

reuse= tf.AUTO_REUSE

with tf.variable_scope("A", reuse=reuse):

    with tf.variable_scope("B", reuse=tf.AUTO_REUSE):

        print tf.get_variable("x", (), tf.float32) # 'A/B/x:0'        

    with tf.variable_scope(None, "B"): # Increment B, as expected

        print tf.get_variable("x", (), tf.float32) # 'A/B_1/x:0'

# Re-enter A and try to increment B

with tf.variable_scope("A", reuse=reuse):    

    with tf.variable_scope(None, "B"): # Does not increment B !!!

        print tf.get_variable("x", (), tf.float32) # 'A/B/x:0' !!!

import tensorflow as tf

tf.variable_scope("A") # This is scope "A"

tf.variable_scope(None, "A") # incremented scope "A_1"

reuse= tf.AUTO_REUSE

with tf.variable_scope("A", reuse=reuse):

    with tf.variable_scope("B", reuse=tf.AUTO_REUSE):

        print tf.get_variable("x", (), tf.float32) # 'A/B/x:0'        

    with tf.variable_scope(None, "B"): # Increment B, as expected

        print tf.get_variable("x", (), tf.float32) # 'A/B_1/x:0'

# Re-enter A and try to increment B

with tf.variable_scope("A", reuse=reuse):    

    with tf.variable_scope(None, "B"): # Does not increment B !!!

        print tf.get_variable("x", (), tf.float32) # 'A/B/x:0' !!!

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