Prompt:
What does this code do?
```python

def map_from_bytes(element):
    return pickle.loads(element)

class TFRecordOptions(PipelineOptions):
    @classmethod
    def _add_argparse_args(cls, parser):

        parser.add_argument(
            "--file_pattern",
            help="A file glob pattern of TFRecord files"
        )


options = TFRecordOptions()
with beam.Pipeline(options=options) as p:

output = (
    p
     "Read from TFRecord" >> ReadFromTFRecord(
        file_pattern=options.file_pattern
    )
    | "Map from bytes" >> Map(map_from_bytes)
    | "Log Data" >> Map(logging.info)
)
```

Response:
This code uses the Apache Beam [TFRecordIO connector](https://beam.apache.org/releases/pydoc/current/apache_beam.io.tfrecordio.html) to read data from a TensorFlow [TFRecord file](https://www.tensorflow.org/api_docs/python/tf/data/TFRecordDataset) matched with `file_pattern`. The `file_pattern` parameter is provided as a command-line argument.

```python
class TFRecordOptions(PipelineOptions):
    @classmethod
    def _add_argparse_args(cls, parser):

        parser.add_argument(
            "--file_pattern",
            help="A file glob pattern of TFRecord files"
        )

options = TFRecordOptions()
```
This code uses [pipeline options](https://beam.apache.org/documentation/patterns/pipeline-options/) to parse command-line arguments.

```python
output = (
    p
     "Read from TFRecord" >> ReadFromTFRecord(
        file_pattern=options.file_pattern
    )
    | "Map from bytes" >> Map(map_from_bytes)
    | "Log Data" >> Map(logging.info)
)
```

The [ReadFromTFRecord transform](https://beam.apache.org/releases/pydoc/current/apache_beam.io.tfrecordio.html#apache_beam.io.tfrecordio.ReadFromTFRecord) reads from a `TFRecord` source. By default, TFRecordIO transforms use `coders.BytesCoder()`. The `Map` transform applies the `map_from_bytes` function to deserialize bytes to a Python object. The Apache Beam pipeline then logs the resulting deserialized object to the console.
