docstr: Returns a new Series with the analyzed Spark DataFrame.
  
  After multiple operations, the underlying Spark plan could grow huge
  and make the Spark planner take a long time to finish the planning.
  
  This function is for the workaround to avoid it.
  
  .. note:: After analyzing, operations between the analyzed Series and the original one
      will **NOT** work without setting a config `compute.ops_on_diff_frames` to `True`.
  
  Returns
  -------
  Series
  
  Examples
  --------
  >>> ser = ps.Series([1, 2, 3])
  >>> ser
  0    1
  1    2
  2    3
  dtype: int64
  
  The analyzed one should return the same value.
  
  >>> ser.spark.analyzed
  0    1
  1    2
  2    3
  dtype: int64
  
  However, it won't work with the same anchor Series.
  
  >>> ser + ser.spark.analyzed
  Traceback (most recent call last):
  ...
  ValueError: ... enable 'compute.ops_on_diff_frames' option.
  
  >>> with ps.option_context('compute.ops_on_diff_frames', True):
  ...     (ser + ser.spark.analyzed).sort_index()
  0    2
  1    4
  2    6
  dtype: int64
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code: def analyzed(self) -> "ps.Series":
      """
      Returns a new Series with the analyzed Spark DataFrame.
  
      After multiple operations, the underlying Spark plan could grow huge
      and make the Spark planner take a long time to finish the planning.
  
      This function is for the workaround to avoid it.
  
      .. note:: After analyzing, operations between the analyzed Series and the original one
          will **NOT** work without setting a config `compute.ops_on_diff_frames` to `True`.
  
      Returns
      -------
      Series
  
      Examples
      --------
      >>> ser = ps.Series([1, 2, 3])
      >>> ser
      0    1
      1    2
      2    3
      dtype: int64
  
      The analyzed one should return the same value.
  
      >>> ser.spark.analyzed
      0    1
      1    2
      2    3
      dtype: int64
  
      However, it won't work with the same anchor Series.
  
      >>> ser + ser.spark.analyzed
      Traceback (most recent call last):
      ...
      ValueError: ... enable 'compute.ops_on_diff_frames' option.
  
      >>> with ps.option_context('compute.ops_on_diff_frames', True):
      ...     (ser + ser.spark.analyzed).sort_index()
      0    2
      1    4
      2    6
      dtype: int64
      """
      from pyspark.pandas.frame import DataFrame
      from pyspark.pandas.series import first_series
  
      return first_series(DataFrame(self._data._internal.resolved_copy))
  
imports: from abc import ABCMeta, abstractmethod
  from typing import TYPE_CHECKING, Callable, Generic, List, Optional, Union
  from pyspark import StorageLevel
  from pyspark.sql import Column, DataFrame
  from pyspark.sql.types import DataType, StructType
  from pyspark.pandas._typing import IndexOpsLike
  from pyspark.pandas.internal import InternalField
  from pyspark.sql.utils import get_column_class, get_dataframe_class
label_desc: Use image node_id 2 for calling the SparkSeriesMethods obj's underlying member method code with example usage: obj.analyzed() and returns: first_series