code: def range(
      start: int,
      end: Optional[int] = None,
      step: int = 1,
      num_partitions: Optional[int] = None,
  ) -> DataFrame:
      """
      Create a DataFrame with some range of numbers.
  
      The resulting DataFrame has a single int64 column named `id`, containing elements in a range
      from ``start`` to ``end`` (exclusive) with step value ``step``. If only the first parameter
      (i.e. start) is specified, we treat it as the end value with the start value being 0.
  
      This is like the range function in SparkSession and is used primarily for testing.
  
      Parameters
      ----------
      start : int
          the start value (inclusive)
      end : int, optional
          the end value (exclusive)
      step : int, optional, default 1
          the incremental step
      num_partitions : int, optional
          the number of partitions of the DataFrame
  
      Returns
      -------
      DataFrame
  
      Examples
      --------
      When the first parameter is specified, we generate a range of values up till that number.
  
      >>> ps.range(5)
         id
      0   0
      1   1
      2   2
      3   3
      4   4
  
      When start, end, and step are specified:
  
      >>> ps.range(start = 100, end = 200, step = 20)
          id
      0  100
      1  120
      2  140
      3  160
      4  180
      """
      sdf = default_session().range(
          start=start, end=end, step=step, numPartitions=num_partitions
      )
      return DataFrame(sdf)
  
imports: from typing import Any, Callable, Dict, List, Optional, Set, Sized, Tuple, Type, Union, cast, no_type_check
  from collections.abc import Iterable
  from datetime import tzinfo
  from functools import reduce
  from io import BytesIO
  import json
  import warnings
  import numpy
  import pandas
  from pandas.api.types import is_datetime64_dtype, is_list_like
  from pandas.tseries.offsets import DateOffset
  import pyarrow
  import pyarrow.parquet
  from pyspark.sql import functions, Column
  from pyspark.sql.functions import pandas_udf
  from pyspark.sql.types import ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType, BooleanType, TimestampType, TimestampNTZType, DecimalType, StringType, DateType, StructType, DataType
  from pyspark.sql.dataframe import DataFrame
  from pyspark import pandas
  from pyspark.pandas._typing import Axis, Dtype, Label, Name
  from pyspark.pandas.base import IndexOpsMixin
  from pyspark.pandas.utils import align_diff_frames, default_session, is_name_like_tuple, is_name_like_value, name_like_string, same_anchor, scol_for, validate_axis, log_advice
  from pyspark.pandas.frame import DataFrame, _reduce_spark_multi
  from pyspark.pandas.internal import InternalFrame, DEFAULT_SERIES_NAME, HIDDEN_COLUMNS, SPARK_INDEX_NAME_FORMAT
  from pyspark.pandas.series import Series, first_series
  from pyspark.pandas.spark.utils import as_nullable_spark_type, force_decimal_precision_scale
  from pyspark.pandas.indexes import Index, DatetimeIndex, TimedeltaIndex
  from pyspark.pandas.indexes.multi import MultiIndex
  from pyspark.sql.utils import get_column_class
label_desc: Use image node_id 2 for calling a global function with example usage: range(start, end, step, num_partitions) and returns: DataFrame