tensorflow_io/python/ops/hdf5_io_tensor_ops.py [134:168]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            )
            if tf.executing_eagerly():
                columns = tf.unstack(columns)
                shapes = [
                    tf.boolean_mask(shape, tf.math.greater_equal(shape, 0))
                    for shape in tf.unstack(shapes)
                ]
                dtypes = [tf.as_dtype(dtype.numpy()) for dtype in tf.unstack(dtypes)]
                entries = [
                    tf.TensorSpec(shape, dtype, column)
                    for (shape, dtype, column) in zip(shapes, dtypes, columns)
                ]
            else:
                assert spec is not None

                entries = spec.items()

                def f(column, columns, shapes):
                    shape = tf.boolean_mask(shapes, tf.math.equal(columns, column))[0]
                    shape = tf.boolean_mask(shape, tf.math.greater_equal(shape, 0))
                    return shape

                shapes = [f(column, columns, shapes) for column, _ in entries]
                dtypes = [
                    entry if isinstance(entry, tf.dtypes.DType) else entry.dtype
                    for _, entry in entries
                ]
                columns = [column for column, _ in entries]

                entries = [
                    tf.TensorSpec(None, dtype, column)
                    for (dtype, column) in zip(dtypes, columns)
                ]

            def g(entry, shape):
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tensorflow_io/python/ops/parquet_io_tensor_ops.py [133:167]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            )
            if tf.executing_eagerly():
                columns = tf.unstack(columns)
                shapes = [
                    tf.boolean_mask(shape, tf.math.greater_equal(shape, 0))
                    for shape in tf.unstack(shapes)
                ]
                dtypes = [tf.as_dtype(dtype.numpy()) for dtype in tf.unstack(dtypes)]
                entries = [
                    tf.TensorSpec(shape, dtype, column)
                    for (shape, dtype, column) in zip(shapes, dtypes, columns)
                ]
            else:
                assert spec is not None

                entries = spec.items()

                def f(column, columns, shapes):
                    shape = tf.boolean_mask(shapes, tf.math.equal(columns, column))[0]
                    shape = tf.boolean_mask(shape, tf.math.greater_equal(shape, 0))
                    return shape

                shapes = [f(column, columns, shapes) for column, _ in entries]
                dtypes = [
                    entry if isinstance(entry, tf.dtypes.DType) else entry.dtype
                    for _, entry in entries
                ]
                columns = [column for column, _ in entries]

                entries = [
                    tf.TensorSpec(None, dtype, column)
                    for (dtype, column) in zip(dtypes, columns)
                ]

            def g(entry, shape):
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