easy_rec/python/compat/feature_column/feature_column.py [3311:3338]:
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    num_buckets = math_ops.cast(
        self.num_buckets, dtypes.int64, name='num_buckets')
    zero = math_ops.cast(0, dtypes.int64, name='zero')
    if self.default_value is None:
      # Fail if values are out-of-range.
      assert_less = check_ops.assert_less(
          values,
          num_buckets,
          data=(values, num_buckets),
          name='assert_less_than_num_buckets')
      assert_greater = check_ops.assert_greater_equal(
          values, zero, data=(values,), name='assert_greater_or_equal_0')
      with ops.control_dependencies((assert_less, assert_greater)):
        values = array_ops.identity(values)
    else:
      # Assign default for out-of-range values.
      values = array_ops.where(
          math_ops.logical_or(
              values < zero, values >= num_buckets, name='out_of_range'),
          array_ops.fill(
              dims=array_ops.shape(values),
              value=math_ops.cast(self.default_value, dtypes.int64),
              name='default_values'), values)

    return sparse_tensor_lib.SparseTensor(
        indices=input_tensor.indices,
        values=values,
        dense_shape=input_tensor.dense_shape)
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easy_rec/python/compat/feature_column/feature_column_v2.py [4270:4297]:
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      num_buckets = math_ops.cast(
          self.num_buckets, dtypes.int64, name='num_buckets')
      zero = math_ops.cast(0, dtypes.int64, name='zero')
      if self.default_value is None:
        # Fail if values are out-of-range.
        assert_less = check_ops.assert_less(
            values,
            num_buckets,
            data=(values, num_buckets),
            name='assert_less_than_num_buckets')
        assert_greater = check_ops.assert_greater_equal(
            values, zero, data=(values,), name='assert_greater_or_equal_0')
        with ops.control_dependencies((assert_less, assert_greater)):
          values = array_ops.identity(values)
      else:
        # Assign default for out-of-range values.
        values = array_ops.where(
            math_ops.logical_or(
                values < zero, values >= num_buckets, name='out_of_range'),
            array_ops.fill(
                dims=array_ops.shape(values),
                value=math_ops.cast(self.default_value, dtypes.int64),
                name='default_values'), values)

    return sparse_tensor_lib.SparseTensor(
        indices=input_tensor.indices,
        values=values,
        dense_shape=input_tensor.dense_shape)
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