class_docstr: Class for resampling timeseries data.
  
  This class is commonly encountered when using ``obj.resample(...)`` which
  return ``Resampler`` objects.
  
  Parameters
  ----------
  obj : Dask DataFrame or Series
      Data to be resampled.
  rule : str, tuple, datetime.timedelta, DateOffset or None
      The offset string or object representing the target conversion.
  kwargs : optional
      Keyword arguments passed to underlying pandas resampling function.
  
  Returns
  -------
  Resampler instance of the appropriate type
docstr: Aggregate using one or more operations
  
  Parameters
  ----------
  how : str
      Name of aggregation operation
  fill_value : scalar, optional
      Value to use for missing values, applied during upsampling.
      Default is NaN.
  how_args : optional
      Positional arguments for aggregation operation.
  how_kwargs : optional
      Keyword arguments for aggregation operation.
  
  Returns
  -------
  Dask DataFrame or Series
docstr_tok: ["Aggregate","using","one","or","more","operations","Parameters","--","--","--","--","--","how",":","str","Name","of","aggregation","operation","fill_value",":","scalar",",","optional","Value","to","use","for","missing","values",",","applied","during","upsampling",".","Default","is","NaN",".","how_args",":","optional","Positional","arguments","for","aggregation","operation",".","how_kwargs",":","optional","Keyword","arguments","for","aggregation","operation",".","Returns","--","--","--","-","Dask","DataFrame","or","Series"]
code: def _agg(
      self,
      how,
      meta=None,
      fill_value=np.nan,
      how_args=(),
      how_kwargs=None,
  ):
      """Aggregate using one or more operations
  
      Parameters
      ----------
      how : str
          Name of aggregation operation
      fill_value : scalar, optional
          Value to use for missing values, applied during upsampling.
          Default is NaN.
      how_args : optional
          Positional arguments for aggregation operation.
      how_kwargs : optional
          Keyword arguments for aggregation operation.
  
      Returns
      -------
      Dask DataFrame or Series
      """
      if how_kwargs is None:
          how_kwargs = {}
  
      rule = self._rule
      kwargs = self._kwargs
      name = "resample-" + tokenize(
          self.obj, rule, kwargs, how, *how_args, **how_kwargs
      )
  
      # Create a grouper to determine closed and label conventions
      newdivs, outdivs = _resample_bin_and_out_divs(
          self.obj.divisions, rule, **kwargs
      )
  
      # Repartition divs into bins. These won't match labels after mapping
      partitioned = self.obj.repartition(newdivs, force=True)
  
      keys = partitioned.__dask_keys__()
      dsk = {}
  
      args = zip(
          keys,
          outdivs,
          outdivs[1:],
          ["left"] * (len(keys) - 1) + [None],
      )
      for i, (k, s, e, c) in enumerate(args):
          dsk[(name, i)] = (
              _resample_series,
              k,
              s,
              e,
              c,
              rule,
              kwargs,
              how,
              fill_value,
              list(how_args),
              how_kwargs,
          )
  
      # Infer output metadata
      meta_r = self.obj._meta_nonempty.resample(
          self._rule, **self._kwargs
      )
      meta = getattr(meta_r, how)(*how_args, **how_kwargs)
  
      graph = HighLevelGraph.from_collections(
          name, dsk, dependencies=[partitioned]
      )
      if isinstance(meta, pd.DataFrame):
          return DataFrame(graph, name, meta, outdivs)
      return Series(graph, name, meta, outdivs)
  
imports: from __future__ import annotations
  import numpy
  import pandas
  from pandas.core.resample import Resampler
  from dask.base import tokenize
  from dask.dataframe import methods
  from dask.dataframe._compat import PANDAS_GE_140
  from dask.dataframe.core import DataFrame, Series
  from dask.highlevelgraph import HighLevelGraph
  from dask.utils import derived_from
label_desc: Use image node_id 2 for calling the Resampler obj's underlying member method code with example usage: obj._agg(how, meta, fill_value, how_args, how_kwargs) and returns: Series, DataFrame