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Prompt:
What does this code do?
```python
class BigtableOptions(PipelineOptions):
@classmethod
def _add_argparse_args(cls, parser):
parser.add_argument(
'--project_id',
required=True,
help='Project ID'
)
parser.add_argument(
'--instance_id',
default="beam-test",
help='Cloud Bigtable instance ID'
)
parser.add_argument(
'--table_id',
default="your-test-table",
help='Cloud Bigtable table ID'
)
options = BigtableOptions()
with beam.Pipeline(options=options) as p:
output = (p | "Read from Bigtable" >> ReadFromBigtable(
project_id=options.project_id,
instance_id=options.instance_id,
table_id=options.table_id
)
| "Extract cells" >> beam.Map(lambda row: row._cells)
| "Log Data" >> Map(logging.info))
```
Response:
This code uses the Apache Beam [BigtableIO connector](https://beam.apache.org/releases/pydoc/current/apache_beam.io.gcp.bigtableio.html) to read data from a [Bigtable](https://cloud.google.com/bigtable/docs) table `table_id` in a Bigtable instance `instance_id`.
The `BigtableOptions` class defines the command-line arguments `project_id`, instance_id’, and `table_id`, which are used to configure the `ReadFromBigtable` transform. These arguments are parsed from the command line using [pipeline options](https://beam.apache.org/documentation/patterns/pipeline-options/).
The `ReadFromBigtable` transform returns a `PCollection` of `PartialRowData` objects, each representing a Bigtable row. For more information about this row object, see [PartialRowData (row_key)](https://cloud.google.com/python/docs/reference/bigtable/latest/row#class-googlecloudbigtablerowpartialrowdatarowkey).
The Apache Beam pipeline extracts the data from a Bigtable row and logs it to the console.
For more information about how to use the BigtableIO connector with the Python SDK for Apache Beam, see [BigtableIO module](https://beam.apache.org/releases/pydoc/current/apache_beam.io.gcp.bigtableio.html).