class_docstr: Pipeline is a tool to create data transformation chains.
  
  Args:
      dag (`dict`): The dag for the pipeline.
      clo_node (`str`): The close node in the pipeline dag, defaults to '_input'.
docstr: Read all rows as single window and perform action.
  
  Args:
      input_schema (tuple): The input column/s of fn.
      output_schema (tuple): The output column/s of fn.
      fn (Operation | lambda | callable): The action to perform on the input_schema after window all data.
      config (dict, optional): Config for the window_all. Defaults to None
  
  Returns:
      Pipeline: Pipeline with window_all action added.
  
  Examples:
      >>> from towhee import pipe
      >>> p = (pipe.input('n1', 'n2')
      ...         .flat_map(('n1', 'n2'), ('n1', 'n2'), lambda x, y: [(a, b) for a, b in zip(x, y)])
      ...         .window_all(('n1', 'n2'), ('s1', 's2'), lambda x, y: (sum(x), sum(y)))
      ...         .output('s1', 's2'))
      >>> p([1, 2, 3, 4], [2, 3, 4, 5]).get()
      [10, 14]
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code: def window_all(
      self, input_schema, output_schema, fn, config=None
  ) -> "Pipeline":
      """
      Read all rows as single window and perform action.
  
      Args:
          input_schema (tuple): The input column/s of fn.
          output_schema (tuple): The output column/s of fn.
          fn (Operation | lambda | callable): The action to perform on the input_schema after window all data.
          config (dict, optional): Config for the window_all. Defaults to None
  
      Returns:
          Pipeline: Pipeline with window_all action added.
  
      Examples:
          >>> from towhee import pipe
          >>> p = (pipe.input('n1', 'n2')
          ...         .flat_map(('n1', 'n2'), ('n1', 'n2'), lambda x, y: [(a, b) for a, b in zip(x, y)])
          ...         .window_all(('n1', 'n2'), ('s1', 's2'), lambda x, y: (sum(x), sum(y)))
          ...         .output('s1', 's2'))
          >>> p([1, 2, 3, 4], [2, 3, 4, 5]).get()
          [10, 14]
      """
      output_schema = self._check_schema(output_schema)
      input_schema = self._check_schema(input_schema)
  
      uid = uuid.uuid4().hex
      fn_action = self._to_action(fn)
      dag_dict = deepcopy(self._dag)
      dag_dict[uid] = {
          "inputs": input_schema,
          "outputs": output_schema,
          "op_info": fn_action.serialize(),
          "iter_info": {
              "type": WindowAllConst.name,
              "param": None,
          },
          "config": config,
          "next_nodes": [],
      }
      dag_dict[self._clo_node]["next_nodes"].append(uid)
      return Pipeline(dag_dict, uid)
  
imports: import uuid
  from copy import deepcopy
  from towhee.runtime.check_utils import TupleForm
  from towhee.runtime.operator_manager import OperatorAction
  from towhee.runtime.factory import _OperatorWrapper
  from towhee.runtime.runtime_pipeline import RuntimePipeline
  from towhee.runtime.constants import MapConst, WindowAllConst, WindowConst, ReduceConst, FilterConst, TimeWindowConst, FlatMapConst, ConcatConst, InputConst, OutputConst, OPName
label_desc: Use image node_id 10 for calling the Pipeline obj's underlying member method code with example usage: obj.window_all(input_schema, output_schema, fn, config) and returns: Pipeline