def _transform_one(transformer, X, y, weight, params=None):
    """Call transform and apply weight to output.

    Parameters
    ----------
    transformer : estimator
        Estimator to be used for transformation.

    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        Input data to be transformed.

    y : ndarray of shape (n_samples,)
        Ignored.

    weight : float
        Weight to be applied to the output of the transformation.

    params : dict
        Parameters to be passed to the transformer's ``transform`` method.

        This should be of the form ``process_routing()["step_name"]``.
    """
    res = transformer.transform(X, **params.transform)
    # if we have a weight for this transformer, multiply output
    if weight is None:
        return res
    return res * weight
Call transform and apply weight to output.

    Parameters
    ----------
    transformer : estimator
        Estimator to be used for transformation.

    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        Input data to be transformed.

    y : ndarray of shape (n_samples,)
        Ignored.

    weight : float
        Weight to be applied to the output of the transformation.

    params : dict
        Parameters to be passed to the transformer's ``transform`` method.

        This should be of the form ``process_routing()["step_name"]``.
    
imblearn/pipeline.py