27187bfff545ef066da5627d7dff8a6caad19e9e,tensorflow_transform/beam/impl.py,TransformDataset,expand,#TransformDataset#Any#,411

Before Change

    output_values = input_values | "MapInstances" >> beam.ParDo(
        _RunMetaGraphDoFn(input_schema, output_schema),
        transform_fn_def=beam.pvalue.AsSingleton(transform_fn))
    return (output_values, output_schema)

After Change

    if self._exclude_outputs is not None:
      schema = output_metadata.schema
      output_metadata = dataset_metadata.DatasetMetadata(
          schema=dataset_schema.Schema(
              {key: column_schema
               for key, column_schema in schema.column_schemas.items()
               if key not in self._exclude_outputs}))

    output_instances = (
        input_values
        | "BatchInstances" >> beam.ParDo(_BatchDoFn())
        | "TransformBatches" >> beam.ParDo(
            _RunMetaGraphDoFn(input_metadata.schema,
                              output_metadata.schema,
                              self._exclude_outputs),
            saved_model_dir=beam.pvalue.AsSingleton(transform_fn))
        | "Unbatch" >> beam.FlatMap(lambda batch: batch))
    return (output_instances, output_metadata)

Instances