title: TensorFlow: TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'
body: <p>Let's say I have this data frame saved in a parquet format</p>
  
  <pre><code>import numpy as np
  import pandas as pd
  
  data = pd.DataFrame(dict(
      a=[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], 
      b=[1.0, 1.0, 1.0, np.NaN, 0.0, np.NaN],
      c=[0.9, np.NaN, 1.0, 0.0, 0.0, 0.0]
  ))
  
  data.to_parquet('data.parquet')
  </code></pre>
  
  <p>along with a dictionary that tells me which values I should use for the imputation. Then I can write a preprocessing function.</p>
  
  <pre><code>import tensorflow as tf
  
  impute_dictionary = dict(b=1.0, c=0.0)
  
  def preprocessing_fn(inputs):
      outputs = inputs.copy()
  
      for key, value in impute_dictionary.items():
          outputs[key] = tf.where(
              tf.math.is_nan(outputs[key]),
              tf.constant(value, shape=outputs[key].shape),
              outputs[key]
          )
  
      return outputs
  </code></pre>
  
  <p>and use it in Apache Beam pipeline</p>
  
  <pre><code>import tempfile
  
  import apache_beam as beam
  import tensorflow_transform.beam as tft_beam
  from tensorflow_transform.tf_metadata import dataset_metadata, schema_utils
  
  temp = tempfile.gettempdir()
  
  RAW_DATA_FEATURE_SPEC = dict(
      [(name, tf.io.FixedLenFeature([], tf.float32)) for name in ['a', 'b', 'c']] 
  )
  
  RAW_DATA_METADATA = dataset_metadata.DatasetMetadata(schema_utils.schema_from_feature_spec(RAW_DATA_FEATURE_SPEC))
  
  with beam.Pipeline() as pipeline:
      with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
          raw_data = pipeline | 'ReadTrainData' &gt;&gt; beam.io.ReadFromParquet('data.parquet')
          raw_dataset = (raw_data, RAW_DATA_METADATA)
          transformed_dataset, transform_fn = (raw_dataset | tft_beam.AnalyzeAndTransformDataset(preprocessing_fn))
          transformed_data, transformed_metadata = transformed_dataset
          transformed_data_coder = tft.coders.ExampleProtoCoder(transformed_metadata.schema)
  </code></pre>
  
  <p>I get this error: <code>TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'</code></p>
  
  <p>It seems that <code>outputs[key].shape</code> is <code>(None,)</code></p>
  
  <p>Any suggestion?</p>
  
  <p>Package versions:</p>
  
  <pre><code>tensorflow==2.1.0
  tensorflow-transform==0.21.0
  pandas==1.0.0
  numpy==1.18.1
  apache-beam==2.19.0
  </code></pre>
  
  <p>Entire error message:</p>
  
  <pre><code>WARNING: Logging before flag parsing goes to stderr.
  W0204 10:36:03.793034 140735593104256 interactive_environment.py:113] Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.
  W0204 10:36:03.793169 140735593104256 interactive_environment.py:125] You have limited Interactive Beam features since your ipython kernel is not connected any notebook frontend.
  W0204 10:36:03.929135 140735593104256 impl.py:360] Tensorflow version (2.1.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 
  W0204 10:36:03.929914 140735593104256 impl.py:360] Tensorflow version (2.1.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 
  ---------------------------------------------------------------------------
  TypeError                                 Traceback (most recent call last)
  &lt;ipython-input-5-465b3f61784c&gt; in &lt;module&gt;
       17         raw_data = pipeline | 'ReadTrainData' &gt;&gt; beam.io.ReadFromParquet('data.parquet')
       18         raw_dataset = (raw_data, RAW_DATA_METADATA)
  ---&gt; 19         transformed_dataset, transform_fn = (raw_dataset | tft_beam.AnalyzeAndTransformDataset(preprocessing_fn))
       20         transformed_data, transformed_metadata = transformed_dataset
       21         transformed_data_coder = tft.coders.ExampleProtoCoder(transformed_metadata.schema)
  
  /usr/local/lib/python3.7/site-packages/apache_beam/transforms/ptransform.py in __ror__(self, left, label)
      547     pvalueish = _SetInputPValues().visit(pvalueish, replacements)
      548     self.pipeline = p
  --&gt; 549     result = p.apply(self, pvalueish, label)
      550     if deferred:
      551       return result
  
  /usr/local/lib/python3.7/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
      575       transform.type_check_inputs(pvalueish)
      576 
  --&gt; 577     pvalueish_result = self.runner.apply(transform, pvalueish, self._options)
      578 
      579     if type_options is not None and type_options.pipeline_type_check:
  
  /usr/local/lib/python3.7/site-packages/apache_beam/runners/runner.py in apply(self, transform, input, options)
      193       m = getattr(self, 'apply_%s' % cls.__name__, None)
      194       if m:
  --&gt; 195         return m(transform, input, options)
      196     raise NotImplementedError(
      197         'Execution of [%s] not implemented in runner %s.' % (transform, self))
  
  /usr/local/lib/python3.7/site-packages/apache_beam/runners/runner.py in apply_PTransform(self, transform, input, options)
      223   def apply_PTransform(self, transform, input, options):
      224     # The base case of apply is to call the transform's expand.
  --&gt; 225     return transform.expand(input)
      226 
      227   def run_transform(self,
  
  /usr/local/lib/python3.7/site-packages/tensorflow_transform/beam/impl.py in expand(self, dataset)
      861     # e.g. caching the values of expensive computations done in AnalyzeDataset.
      862     transform_fn = (
  --&gt; 863         dataset | 'AnalyzeDataset' &gt;&gt; AnalyzeDataset(self._preprocessing_fn))
      864 
      865     if Context.get_use_deep_copy_optimization():
  
  /usr/local/lib/python3.7/site-packages/apache_beam/transforms/ptransform.py in __ror__(self, pvalueish, _unused)
      987 
      988   def __ror__(self, pvalueish, _unused=None):
  --&gt; 989     return self.transform.__ror__(pvalueish, self.label)
      990 
      991   def expand(self, pvalue):
  
  /usr/local/lib/python3.7/site-packages/apache_beam/transforms/ptransform.py in __ror__(self, left, label)
      547     pvalueish = _SetInputPValues().visit(pvalueish, replacements)
      548     self.pipeline = p
  --&gt; 549     result = p.apply(self, pvalueish, label)
      550     if deferred:
      551       return result
  
  /usr/local/lib/python3.7/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
      534       try:
      535         old_label, transform.label = transform.label, label
  --&gt; 536         return self.apply(transform, pvalueish)
      537       finally:
      538         transform.label = old_label
  
  /usr/local/lib/python3.7/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
      575       transform.type_check_inputs(pvalueish)
      576 
  --&gt; 577     pvalueish_result = self.runner.apply(transform, pvalueish, self._options)
      578 
      579     if type_options is not None and type_options.pipeline_type_check:
  
  /usr/local/lib/python3.7/site-packages/apache_beam/runners/runner.py in apply(self, transform, input, options)
      193       m = getattr(self, 'apply_%s' % cls.__name__, None)
      194       if m:
  --&gt; 195         return m(transform, input, options)
      196     raise NotImplementedError(
      197         'Execution of [%s] not implemented in runner %s.' % (transform, self))
  
  /usr/local/lib/python3.7/site-packages/apache_beam/runners/runner.py in apply_PTransform(self, transform, input, options)
      223   def apply_PTransform(self, transform, input, options):
      224     # The base case of apply is to call the transform's expand.
  --&gt; 225     return transform.expand(input)
      226 
      227   def run_transform(self,
  
  /usr/local/lib/python3.7/site-packages/tensorflow_transform/beam/impl.py in expand(self, dataset)
      808     input_values, input_metadata = dataset
      809     result, cache = super(AnalyzeDataset, self).expand((input_values, None,
  --&gt; 810                                                         None, input_metadata))
      811     assert not cache
      812     return result
  
  /usr/local/lib/python3.7/site-packages/tensorflow_transform/beam/impl.py in expand(self, dataset)
      681         copied_inputs = impl_helper.copy_tensors(input_signature)
      682 
  --&gt; 683       output_signature = self._preprocessing_fn(copied_inputs)
      684 
      685     # At this point we check that the preprocessing_fn has at least one
  
  &lt;ipython-input-2-205d9abf4136&gt; in preprocessing_fn(inputs)
        9         outputs[key] = tf.where(
       10             tf.math.is_nan(outputs[key]),
  ---&gt; 11             tf.constant(value, shape=outputs[key].shape),
       12             outputs[key]
       13         )
  
  /usr/local/lib/python3.7/site-packages/tensorflow_core/python/framework/constant_op.py in constant(value, dtype, shape, name)
      256   """
      257   return _constant_impl(value, dtype, shape, name, verify_shape=False,
  --&gt; 258                         allow_broadcast=True)
      259 
      260 
  
  /usr/local/lib/python3.7/site-packages/tensorflow_core/python/framework/constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
      294       tensor_util.make_tensor_proto(
      295           value, dtype=dtype, shape=shape, verify_shape=verify_shape,
  --&gt; 296           allow_broadcast=allow_broadcast))
      297   dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
      298   const_tensor = g._create_op_internal(  # pylint: disable=protected-access
  
  /usr/local/lib/python3.7/site-packages/tensorflow_core/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape, allow_broadcast)
      446     # If shape is None, numpy.prod returns None when dtype is not set, but
      447     # raises exception when dtype is set to np.int64
  --&gt; 448     if shape is not None and np.prod(shape, dtype=np.int64) == 0:
      449       nparray = np.empty(shape, dtype=np_dt)
      450     else:
  
  &lt;__array_function__ internals&gt; in prod(*args, **kwargs)
  
  /usr/local/lib/python3.7/site-packages/numpy/core/fromnumeric.py in prod(a, axis, dtype, out, keepdims, initial, where)
     2960     """
     2961     return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,
  -&gt; 2962                           keepdims=keepdims, initial=initial, where=where)
     2963 
     2964 
  
  /usr/local/lib/python3.7/site-packages/numpy/core/fromnumeric.py in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs)
       88                 return reduction(axis=axis, out=out, **passkwargs)
       89 
  ---&gt; 90     return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
       91 
       92 
  
  TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'
  </code></pre>