You are given the following PySpark DataFrame storesDF:

from pyspark.sql import Row

data = [
    Row(id=1, name="Store A", details=Row(sqft=1200, street="Main Street")),
    Row(id=2, name="Store B", details=Row(sqft=1500, street="Broadway"))
]

storesDF = spark.createDataFrame(data)
storesDF.printSchema()

from pyspark.sql import Row

data = [
    Row(id=1, name="Store A", details=Row(sqft=1200, street="Main Street")),
    Row(id=2, name="Store B", details=Row(sqft=1500, street="Broadway"))
]

storesDF = spark.createDataFrame(data)
storesDF.printSchema()

Schema:

root
 |-- id: long (nullable = true)
 |-- name: string (nullable = true)
 |-- details: struct (nullable = true)
 |    |-- sqft: long (nullable = true)
 |    |-- street: string (nullable = true)

root
 |-- id: long (nullable = true)
 |-- name: string (nullable = true)
 |-- details: struct (nullable = true)
 |    |-- sqft: long (nullable = true)
 |    |-- street: string (nullable = true)

You need to extract the sqft value from the details struct of the first row as a Python int.

Fill in the blanks in the following code: int(__1__.__2__[__3__][__4__])