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import os
import shutil
import tensorflow as tf
import tensorflow_datasets as tfds
import tabnet
import pandas as pd
import numpy as np

train_size = 125
BATCH_SIZE = 50

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W0401 13:47:33.782922 140134609663744 basic_session_run_hooks.py:732] It seems that global step (tf.train.get_global_step) 
has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by pass
ing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.                                         
*** Error in `python': malloc(): memory corruption (fast): 0x00007f6f78326510 ***                                          
======= Backtrace: =========                                                                                               
/lib/x86_64-linux-gnu/libc.so.6(+0x70bfb)[0x7f73a0777bfb]                                                                  
/lib/x86_64-linux-gnu/libc.so.6(+0x76fc6)[0x7f73a077dfc6]                                                                  
/lib/x86_64-linux-gnu/libc.so.6(+0x79491)[0x7f73a0780491]                                                                  
/lib/x86_64-linux-gnu/libc.so.6(

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### Diagnostics

<details>
<summary>Diagnostics output</summary>

``````
--- check: autoidentify
INFO: diagnose_tensorboard.py version d515ab103e2b1cfcea2b096187741a0eeb8822ef

--- check: general

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import tensorflow as tf
from tensorflow import feature_column as fc
import pandas as pd
import numpy as np


FC_DISPATCH = {
    'numeric': lambda key, training_vals, **kwargs: fc.numeric_column(key, **kwargs),
    'categorical': lambda key, training_vals, **kwargs: fc.indicator_column(
        fc.categorical_column_with_vocabulary_list(

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   jax_jit_correlation  jax_jit_time  jax_nojit_correlation  jax_nojit_time  np_copy_correlation  np_copy_time  np_inplace_correlation  np_inplace_time      size
0            -0.087945      0.520224              -0.207465        0.515517            -0.086613      0.000577               -0.048701         0.000638       100
1             0.009647      0.409452              -0.009721        0.407500            -0.016213      0.000776               -0.032633         0.000694      1000
2             0.005357      0.652246               0.010250        0.633742             0.002910      0.003117               -0.001414         0.001872     10000
3            -0.002672      1.441106               0.000594        1.425177             0.004118      0.023031               -0.004778         0.017846    100000
4             0.001152     11.028948               0.000698       10.984871            -0.002028      0.228054                0.001034         0.163242   1000000
5             0.000007    250.970225              -0

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import numpy as onp
import jax.numpy as jnp
import jax.random as jrand
from jax import jit

@jit
def jax_shuffler(all_inputs, key):
    shuffled_input = jrand.shuffle(key, all_inputs, axis=0)
    return shuffled_input

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Traceback (most recent call last):
  File "jax_models.py", line 232, in <module>
    shuffle=True,
  File "jax_models.py", line 181, in fit
    voter_indices, target_indices, ratings, batch_size, batched_dataset_size)
  File "/home/u1/zach/proj/dataplayground3/lib/python3.5/site-packages/jax/api.py", line 150, in f_jitted
    out = xla.xla_call(flat_fun, *args_flat, device=device, backend=backend)
  File "/home/u1/zach/proj/dataplayground3/lib/python3.5/site-packages/jax/core.py", line 592, in call_bind
    outs = primitive.impl(f, *args, **params)
  File "/home/u1/zach/proj/dataplayground3/lib/python3.5/site-packages/jax/interpreters/xla.py", line 400, in _xla_call_impl

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{'error-code': 'INVALID_APPLICATION_PACKAGE',
 'message': 'Invalid application package: default.default: Error loading '
            'model: Could not import TensorFlow model from directory '
            "'/opt/vespa/var/db/vespa/config_server/serverdb/tenants/default/sessions/175/.preprocessed/models/plike_test/tf114_export': "
            "_output_shapes attribute of 'init_1' does not exist"}

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import keras                                                                           
import numpy as np                                                                     
                                                                                       
input_l = keras.Input(shape=(1,), name='input')                                        
layer_1 = keras.layers.Dense(1, activation='relu', name='layer_1')(input_l)            
output_l = keras.layers.Dense(1, activation='linear', name='output')(layer_1)          
model = keras.Model(inputs=[input_l], outputs=[output_l])                              
model.compile(loss='mean_absolute_error', optimizer='rmsprop')                         
                                                                                       
x = np.array(np.arange(1, 100000))                                                     

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import tensorflow as tf                                                                  
                                                                                         
# Create a tf.keras model.                                                               
print(tf.version.VERSION)                                                                
model = tf.keras.Sequential()                                                            
model.add(tf.keras.layers.Dense(1, input_shape=[10]))                                    
model.summary()                                                                          
                                                                                         
# Save the tf.keras model in the SavedModel format.                                      
saved_to_path = tf.keras.experimental.export(