class_docstr: Utils for generating linear data.
  
  .. versionadded:: 1.5.0
docstr: Generate an RDD of LabeledPoints.
docstr_tok: ["Generate","an","RDD","of","LabeledPoints","."]
code: def generateLinearRDD(
      sc: SparkContext,
      nexamples: int,
      nfeatures: int,
      eps: float,
      nParts: int = 2,
      intercept: float = 0.0,
  ) -> RDD["LabeledPoint"]:
      """
      Generate an RDD of LabeledPoints.
      """
      return callMLlibFunc(
          "generateLinearRDDWrapper",
          sc,
          int(nexamples),
          int(nfeatures),
          float(eps),
          int(nParts),
          float(intercept),
      )
  
imports: import sys
  from functools import reduce
  import numpy
  from pyspark import SparkContext, since
  from pyspark.mllib.common import callMLlibFunc, inherit_doc
  from pyspark.mllib.linalg import Vectors, SparseVector, _convert_to_vector
  from pyspark.sql import DataFrame
  from typing import Generic, Iterable, List, Optional, Tuple, Type, TypeVar, cast, TYPE_CHECKING
  from pyspark.context import SparkContext
  from pyspark.mllib.linalg import Vector
  from pyspark.rdd import RDD
  from pyspark.sql.dataframe import DataFrame
label_desc: Use image node_id 2 for calling the LinearDataGenerator obj's underlying member method code with example usage: obj.generateLinearRDD(sc, nexamples, nfeatures, eps, nParts, intercept) and returns: callMLlibFunc