assert response.status_code == 422, response.text
    # insert_assert(response.json())
    assert response.json() == {
        "detail": [
            {
                "type": "string_type",
                "loc": ["tags", 3],
                "msg": "Input should be a valid string",
                "input": {"sneaky": "object"},
            }
        ]
    }


@needs_pydanticv2

		// simply a read on JSON from MinIO and write it back as "parquet".
		//
		// import org.apache.spark.sql.SparkSession
		// import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
		// object SparkJSONRead {
		//   def main(args: Array[String]): Unit = {
		//     val spark:SparkSession = SparkSession.builder()
		//       .appName("SparkByExample")

or assignment. For instance, <code>int32</code> and <code>int</code>
are not the same type even though they may have the same size on a
particular architecture.
</p>

<h3 id="String_types">String types</h3>

<p>
A <i>string type</i> represents the set of string values.
A string value is a (possibly empty) sequence of bytes.
The number of bytes is called the length of the string and is never negative.

or assignment. For instance, <code>int32</code> and <code>int</code>
are not the same type even though they may have the same size on a
particular architecture.
</p>

<h3 id="String_types">String types</h3>

<p>
A <i>string type</i> represents the set of string values.
A string value is a (possibly empty) sequence of bytes.
The number of bytes is called the length of the string and is never negative.