<jupyter_start><jupyter_text><jupyter_code>!pip install tensorflow==2.0.0-alpha0
import tensorflow as tf
tf.__version__
with tf.name_scope("Hello"):
  nice = tf.constant([1,2], name= "kiki")
  

inputs = tf.keras.Input(shape=[2])
with tf.name_scope('block'):
    outputs = tf.keras.layers.Dense(10)(inputs)
model = tf.keras.Model(inputs, outputs)
for w in model.weights:
    print(w.name)
with tf.Graph().as_default() as ho:
  ni = tf.constant([1,2,3], name="pkmkb")
  
ni
with tf.Graph().as_default() as ko:
  with tf.name_scope("i_am_with_u"):
    new_var = tf.constant([5,6,7],name= "hoola")
    
  with ko.name_scope("again"):
    oppo = tf.constant([8,9], name= "another_one")
    
print(new_var)
print(oppo)
with ko.name_scope("niche"):
  un_oppo = tf.constant([8,9,46], name= "one")
  
print(un_oppo)



with tf.Graph().as_default():  
  inputs = tf.keras.Input(shape=[2])
  with tf.name_scope('block'):
      outputs = tf.keras.layers.Dense(10)(inputs)
model = tf.keras.Model(inputs, outputs)
for w in model.weights:
    print(w.name)
f"""kfmokvnfdv"""<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.181651376146789, 'fraction_numerical': 0.022018348623853212, 'mean_word_length': 4.682291666666667, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '572054', 'license': 'Apache-2.0', 'n_tokens_mistral': 448, 'n_tokens_neox': 443, 'n_words': 77, 'path': 'tf_issue#27298.ipynb', 'repo_name': 'gshashank84/learn-tf', 'size': '1090'}