(
        self, X, y=None, unsafe=False, classes=None, **fit_params
    )
return new_pipeline
partial_fit for a pipeline.
        This method assumes that all but the last node of a pipeline are frozen_trained and
        only the last node needs to be fit using its partial_fit method.

        Parameters
        ----------
        X :
            Features; see partial_fit schema of the last node.
        y:
            Labels/target
        unsafe:
            boolean.
            This flag allows users to override the validation that throws an error when the
            the operators in the prefix of this pipeline are not tagged with `has_partial_transform`.
            Setting unsafe to True would perform the transform as if it was row-wise even in the case it may not be.
        fit_params:
            dict
            Additional keyword arguments to be passed to partial_fit of the estimator
        classes:

        Returns
        -------
        TrainedPipeline :
            A partially trained pipeline, which can be trained further by other calls to partial_fit
partial_fit for a pipeline.
        This method assumes that all but the last node of a pipeline are frozen_trained and
        only the last node needs to be fit using its partial_fit method.
partial_fit for a pipeline . This method assumes that all but the last node of a pipeline are frozen_trained and only the last node needs to be fit using its partial_fit method .
def partial_fit(
        self, X, y=None, unsafe=False, classes=None, **fit_params
    ) -> "TrainedPipeline[TrainedIndividualOp]":
        """partial_fit for a pipeline.
        This method assumes that all but the last node of a pipeline are frozen_trained and
        only the last node needs to be fit using its partial_fit method.

        Parameters
        ----------
        X :
            Features; see partial_fit schema of the last node.
        y:
            Labels/target
        unsafe:
            boolean.
            This flag allows users to override the validation that throws an error when the
            the operators in the prefix of this pipeline are not tagged with `has_partial_transform`.
            Setting unsafe to True would perform the transform as if it was row-wise even in the case it may not be.
        fit_params:
            dict
            Additional keyword arguments to be passed to partial_fit of the estimator
        classes:

        Returns
        -------
        TrainedPipeline :
            A partially trained pipeline, which can be trained further by other calls to partial_fit

        """
        estimator_only = True

        for operator in self._steps[:-1]:
            if not operator.is_frozen_trained():
                estimator_only = False
        if not estimator_only:
            raise ValueError(
                """partial_fit is only supported on pipelines when all but the last node are frozen_trained and
        only the last node needs to be fit using its partial_fit method."""
            )

        sink_node = self._steps[-1]
        self.remove_last(inplace=True)
        transformed_output = self.transform(X, y)
        if isinstance(transformed_output, tuple):
            transformed_X, transformed_y = transformed_output
        else:
            transformed_X = transformed_output
            transformed_y = y
        trained_sink_node = sink_node.partial_fit(
            transformed_X, transformed_y, **fit_params
        )  # note: no classes being passed here as we assume the trained pipeline is obtained after a call to partial_fit
        new_pipeline = self >> trained_sink_node
        return new_pipeline
def partial_fit ( self , X , y = None , unsafe = False , classes = None , * * fit_params ) -> "TrainedPipeline[TrainedIndividualOp]" : estimator_only = True for operator in self . _steps [ : - 1 ] : if not operator . is_frozen_trained ( ) : estimator_only = False if not estimator_only : raise ValueError ( """partial_fit is only supported on pipelines when all but the last node are frozen_trained and
        only the last node needs to be fit using its partial_fit method.""" ) sink_node = self . _steps [ - 1 ] self . remove_last ( inplace = True ) transformed_output = self . transform ( X , y ) if isinstance ( transformed_output , tuple ) : transformed_X , transformed_y = transformed_output else : transformed_X = transformed_output transformed_y = y trained_sink_node = sink_node . partial_fit ( transformed_X , transformed_y , * * fit_params ) # note: no classes being passed here as we assume the trained pipeline is obtained after a call to partial_fit new_pipeline = self >> trained_sink_node return new_pipeline