class_docstr: Wrapper step to transform both ids, data inputs, and expected output at the same time using classical fit and transform methods.
  It sends the data_inputs, and the expected_outputs to the wrapped step so that it can transform them.
  
  .. seealso::
      :class:`~neuraxle.base.BaseStep`,
      :class:`~neuraxle.base.ForceHandleOnlyMixin`
docstr: Handle fit by passing the data inputs, and the expected outputs to the wrapped step fit method.
  
  :param context: execution context
  :type context: ExecutionContext
  :param data_container: data container to fit on
  :return: self, data container
docstr_tok: ["Handle","fit","by","passing","the","data","inputs",",","and","the","expected","outputs","to","the","wrapped","step","fit","method",".",":","param","context",":","execution","context",":","type","context",":","ExecutionContext",":","param","data_container",":","data","container","to","fit","on",":","return",":","self",",","data","container"]
code: def _fit_data_container(
      self, data_container: DACT, context: CX
  ) -> Tuple[BaseStep, DACT]:
      """
      Handle fit by passing the data inputs, and the expected outputs to the wrapped step fit method.
  
      :param context: execution context
      :type context: ExecutionContext
      :param data_container: data container to fit on
      :return: self, data container
      """
      self.wrapped = self.wrapped.handle_fit(
          DACT(
              ids=data_container._ids,
              data_inputs=(
                  copy.copy(data_container._ids),
                  copy.copy(data_container.data_inputs),
                  copy.copy(data_container.expected_outputs),
              ),
              expected_outputs=None,
          ),
          context,
      )
  
      data_container.set_data_inputs(
          (
              data_container._ids,
              data_container.data_inputs,
              data_container.expected_outputs,
          )
      )
      data_container.set_expected_outputs(
          expected_outputs=None
      )  # TODO: thy this?
  
      return self
  
imports: import copy
  from typing import Tuple
  from neuraxle.base import BaseStep
  from neuraxle.base import ExecutionContext
  from neuraxle.base import ForceHandleOnlyMixin, MetaStep, MixinForBaseTransformer
  from neuraxle.data_container import DataContainer
label_desc: Use image node_id 3 for calling the IdsInputAndOutputTransformerWrapper obj's underlying member method code with example usage: obj._fit_data_container(data_container, context) and returns: self