tensorflow: TF 2.0 crossed_column on Windows fails with SystemError: returned a result with an error set

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System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10 Home
• Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
• TensorFlow installed from (source or binary): binary
• TensorFlow version (use command below): v1.12.0-9492-g2c319fb415 2.0.0-alpha0
• Python version: 3.6.8 |Anaconda, Inc.| (default, Feb 21 2019, 18:30:04) [MSC v.1916 64 bit (AMD64)]
• Bazel version (if compiling from source): NA
• GCC/Compiler version (if compiling from source): NA
• CUDA/cuDNN version: NA
• GPU model and memory: NA

You can collect some of this information using our environment capture script You can also obtain the TensorFlow version with: 1. TF 1.0: python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)" 2. TF 2.0: python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"

Describe the current behavior The code snippets works fine on Colab but gives the following error on Windows:

OverflowError: Python int too large to convert to C long
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "C:\Users\user\Anaconda3\envs\tf2\lib\site-packages\IPython\core\interactiveshell.py", line 3296, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-6-6cfdea12e863>", line 44, in <module>
    demo(feature_column.indicator_column(crossed_feature))
  File "<ipython-input-6-6cfdea12e863>", line 36, in demo
    print(feature_layer(example_batch))
  File "C:\Users\user\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 660, in __call__
    outputs = self.call(inputs, *args, **kwargs)
  File "C:\Users\user\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\feature_column\feature_column_v2.py", line 473, in call
    self._state_manager)
  File "C:\Users\user\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\feature_column\feature_column_v2.py", line 4391, in get_dense_tensor
    return transformation_cache.get(self, state_manager)
  File "C:\Users\user\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\feature_column\feature_column_v2.py", line 2573, in get
    transformed = column.transform_feature(self, state_manager)
  File "C:\Users\user\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\feature_column\feature_column_v2.py", line 4330, in transform_feature
    transformation_cache, state_manager)
  File "C:\Users\user\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\feature_column\feature_column_v2.py", line 4184, in get_sparse_tensors
    transformation_cache.get(self, state_manager), None)
  File "C:\Users\user\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\feature_column\feature_column_v2.py", line 2573, in get
    transformed = column.transform_feature(self, state_manager)
  File "C:\Users\user\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\feature_column\feature_column_v2.py", line 4145, in transform_feature
    hash_key=self.hash_key)
  File "C:\Users\user\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\ops\sparse_ops.py", line 564, in sparse_cross_hashed
    name=name)
  File "C:\Users\user\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\ops\sparse_ops.py", line 617, in _sparse_cross_internal
    name=name)
  File "C:\Users\user\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\ops\gen_sparse_ops.py", line 1372, in sparse_cross
    "internal_type", internal_type)
SystemError: <built-in function TFE_Py_FastPathExecute> returned a result with an error set

Executing:

import sys
sys.maxsize

gives: 9223372036854775807

Describe the expected behavior Same output as running on Colab:

Code to reproduce the issue

import tensorflow as tf
import pandas as pd

from tensorflow import feature_column
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split

URL = 'https://storage.googleapis.com/applied-dl/heart.csv'
dataframe = pd.read_csv(URL)
dataframe.head()

train, test = train_test_split(dataframe, test_size=0.2)
train, val = train_test_split(train, test_size=0.2)

def df_to_dataset(dataframe, shuffle=True, batch_size=32):
  dataframe = dataframe.copy()
  labels = dataframe.pop('target')
  ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
  if shuffle:
    ds = ds.shuffle(buffer_size=len(dataframe))
  ds = ds.batch(batch_size)
  return ds


batch_size = 5 # A small batch sized is used for demonstration purposes
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)

example_batch = next(iter(train_ds))[0]

# A utility method to create a feature column
# and to transform a batch of data
def demo(feature_column):
  feature_layer = layers.DenseFeatures(feature_column)
  print(feature_layer(example_batch))

age = feature_column.numeric_column("age")
age_buckets = feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
thal = feature_column.categorical_column_with_vocabulary_list(
      'thal', ['fixed', 'normal', 'reversible'])

crossed_feature = feature_column.crossed_column([age_buckets, thal], hash_bucket_size=3)
demo(feature_column.indicator_column(crossed_feature))

Other info / logs Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.