How can I use the `from_xml` and `schema_of_xml` functions in PySpark to parse nested XML data and create a DataFrame schema?
You can use the `from_xml` and `schema_of_xml` functions in PySpark to parse nested XML data and create a DataFrame schema as follows:

First, you need to import the necessary functions:

```python
from pyspark.sql.functions import from_xml, schema_of_xml, lit, col
```

Next, create a DataFrame with your XML data:

```python
xml_data = """
<book id="bk103">
  <author>Corets, Eva</author>
  <title>Maeve Ascendant</title>
  <genre>Fantasy</genre>
  <price>5.95</price>
  <publish_date>2000-11-17</publish_date>
</book>
"""

df = spark.createDataFrame([(8, xml_data)], ["number", "payload"])
```

Then, generate the schema from the XML data:

```python
schema = schema_of_xml(df.select("payload").limit(1).collect()[0][0])
```

Finally, parse the XML data using the generated schema:

```python
parsed = df.withColumn("parsed", from_xml(col("payload"), schema))
parsed.printSchema()
parsed.show()
```

This will create a new DataFrame (`parsed`) with the parsed XML data and the corresponding schema.