class_docstr: A collection of data which can be pre-processed in parallel on Spark.
class_docstr_tok: ["A","collection","of","data","which","can","be","pre-processed","in","parallel","on","Spark","."]
docstr: Retrun first rows of the first element of a SparkXShards.
  
  :param n: int, default 5
  :return: same type as self.type['class_name']
      The first `n` rows of the first element of this SparkXShards.
docstr_tok: ["Retrun","first","rows","of","the","first","element","of","a","SparkXShards",".",":","param","n",":","int",",","default","5",":","return",":","same","type","as","self.type","[","'class_name","'","]","The","first","`","n","`","rows","of","the","first","element","of","this","SparkXShards","."]
code: def head(self, n: int = 5) -> "PandasDataFrame":
      """
      Retrun first rows of the first element of a SparkXShards.
  
      :param n: int, default 5
      :return: same type as self.type['class_name']
          The first `n` rows of the first element of this SparkXShards.
      """
  
      return self.rdd.first().head(n)
  
imports: from py4j.protocol import Py4JError
  from bigdl.orca.data.utils import *
  from bigdl.orca import OrcaContext
  from bigdl.dllib.utils.common import get_node_and_core_number, get_spark_sql_context, get_spark_context
  from bigdl.dllib.utils import nest
  from bigdl.dllib.utils.log4Error import invalidInputError
  import numpy
  from typing import TYPE_CHECKING, Any
  from typing import Callable, Dict, List, Optional, Tuple, Union
label_desc: Use image node_id 43 for calling the SparkXShards obj's underlying member method code with example usage: obj.head(n) and returns: self