docstr: Compute values for ``pipeline`` from ``start_date`` to ``end_date``, in
  date chunks of size ``chunksize``.
  
  Chunked execution reduces memory consumption, and may reduce
  computation time depending on the contents of your pipeline.
  
  Parameters
  ----------
  pipeline : Pipeline
      The pipeline to run.
  start_date : pd.Timestamp
      The start date to run the pipeline for.
  end_date : pd.Timestamp
      The end date to run the pipeline for.
  chunksize : int
      The number of days to execute at a time.
  hooks : list[implements(PipelineHooks)], optional
      Hooks for instrumenting Pipeline execution.
  
  Returns
  -------
  result : pd.DataFrame
      A frame of computed results.
  
      The ``result`` columns correspond to the entries of
      `pipeline.columns`, which should be a dictionary mapping strings to
      instances of :class:`zipline.pipeline.Term`.
  
      For each date between ``start_date`` and ``end_date``, ``result``
      will contain a row for each asset that passed `pipeline.screen`.
      A screen of ``None`` indicates that a row should be returned for
      each asset that existed each day.
  
  See Also
  --------
  :meth:`zipline.pipeline.engine.PipelineEngine.run_pipeline`
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code: def run_chunked_pipeline(
      self, pipeline, start_date, end_date, chunksize, hooks=None
  ):
      """
      Compute values for ``pipeline`` from ``start_date`` to ``end_date``, in
      date chunks of size ``chunksize``.
  
      Chunked execution reduces memory consumption, and may reduce
      computation time depending on the contents of your pipeline.
  
      Parameters
      ----------
      pipeline : Pipeline
          The pipeline to run.
      start_date : pd.Timestamp
          The start date to run the pipeline for.
      end_date : pd.Timestamp
          The end date to run the pipeline for.
      chunksize : int
          The number of days to execute at a time.
      hooks : list[implements(PipelineHooks)], optional
          Hooks for instrumenting Pipeline execution.
  
      Returns
      -------
      result : pd.DataFrame
          A frame of computed results.
  
          The ``result`` columns correspond to the entries of
          `pipeline.columns`, which should be a dictionary mapping strings to
          instances of :class:`zipline.pipeline.Term`.
  
          For each date between ``start_date`` and ``end_date``, ``result``
          will contain a row for each asset that passed `pipeline.screen`.
          A screen of ``None`` indicates that a row should be returned for
          each asset that existed each day.
  
      See Also
      --------
      :meth:`zipline.pipeline.engine.PipelineEngine.run_pipeline`
      """
      raise NotImplementedError("run_chunked_pipeline")
  
imports: from abc import ABCMeta, abstractmethod
  from functools import partial
  from six import iteritems, with_metaclass, viewkeys
  from numpy import array, arange
  from pandas import DataFrame, MultiIndex
  from toolz import groupby
  from zipline.lib.adjusted_array import ensure_adjusted_array, ensure_ndarray
  from zipline.errors import NoFurtherDataError
  from zipline.utils.input_validation import expect_types
  from zipline.utils.numpy_utils import as_column, repeat_first_axis, repeat_last_axis
  from zipline.utils.pandas_utils import explode
  from zipline.utils.string_formatting import bulleted_list
  from .domain import Domain, GENERIC
  from .graph import maybe_specialize
  from .hooks import DelegatingHooks
  from .term import AssetExists, InputDates, LoadableTerm
  from zipline.utils.date_utils import compute_date_range_chunks
  from zipline.utils.pandas_utils import categorical_df_concat
label_desc: Use image node_id 2 for calling the PipelineEngine obj's underlying member method code with example usage: obj.run_chunked_pipeline(pipeline, start_date, end_date, chunksize, hooks) without return types