class_docstr: A base class for data source readers. Data source readers are responsible for
  outputting data from a data source.
  
  .. versionadded: 4.0.0
docstr: Returns an iterator of partitions for this data source.
  
  Partitions are used to split data reading operations into parallel tasks.
  If this method returns N partitions, the query planner will create N tasks.
  Each task will execute ``read(partition)`` in parallel, using the respective
  partition value to read the data.
  
  This method is called once during query planning. By default, it returns a
  single partition with the value ``None``. Subclasses can override this method
  to return multiple partitions.
  
  It's recommended to override this method for better performance when reading
  large datasets.
  
  Returns
  -------
  Sequence[InputPartition]
      A sequence of partitions for this data source. Each partition value
      must be an instance of `InputPartition` or a subclass of it.
  
  Notes
  -----
  All partition values must be picklable objects.
  
  Examples
  --------
  Returns a list of integers:
  
  >>> def partitions(self):
  ...     return [InputPartition(1), InputPartition(2), InputPartition(3)]
  
  Returns a list of string:
  
  >>> def partitions(self):
  ...     return [InputPartition("a"), InputPartition("b"), InputPartition("c")]
  
  Returns a list of ranges:
  
  >>> class RangeInputPartition(InputPartition):
  ...    def __init__(self, start, end):
  ...        self.start = start
  ...        self.end = end
  
  >>> def partitions(self):
  ...     return [RangeInputPartition(1, 3), RangeInputPartition(5, 10)]
code: def partitions(self) -> Sequence[InputPartition]:
      """
      Returns an iterator of partitions for this data source.
  
      Partitions are used to split data reading operations into parallel tasks.
      If this method returns N partitions, the query planner will create N tasks.
      Each task will execute ``read(partition)`` in parallel, using the respective
      partition value to read the data.
  
      This method is called once during query planning. By default, it returns a
      single partition with the value ``None``. Subclasses can override this method
      to return multiple partitions.
  
      It's recommended to override this method for better performance when reading
      large datasets.
  
      Returns
      -------
      Sequence[InputPartition]
          A sequence of partitions for this data source. Each partition value
          must be an instance of `InputPartition` or a subclass of it.
  
      Notes
      -----
      All partition values must be picklable objects.
  
      Examples
      --------
      Returns a list of integers:
  
      >>> def partitions(self):
      ...     return [InputPartition(1), InputPartition(2), InputPartition(3)]
  
      Returns a list of string:
  
      >>> def partitions(self):
      ...     return [InputPartition("a"), InputPartition("b"), InputPartition("c")]
  
      Returns a list of ranges:
  
      >>> class RangeInputPartition(InputPartition):
      ...    def __init__(self, start, end):
      ...        self.start = start
      ...        self.end = end
  
      >>> def partitions(self):
      ...     return [RangeInputPartition(1, 3), RangeInputPartition(5, 10)]
      """
      raise PySparkNotImplementedError(
          error_class="NOT_IMPLEMENTED",
          message_parameters={"feature": "partitions"},
      )
  
imports: from abc import ABC, abstractmethod
  from collections import UserDict
  from typing import Any, Dict, Iterator, List, Sequence, Tuple, Type, Union, TYPE_CHECKING
  from pyspark.sql import Row
  from pyspark.sql.types import StructType
  from pyspark.errors import PySparkNotImplementedError
label_desc: Use image node_id 1 for calling the DataSourceReader obj's underlying member method code with example usage: obj.partitions() without return types