def fit(self, Z, **fit_params):
fit_params_steps = dict((step, {})<EOL>for step, _ in self.transformer_list)<EOL>for pname, pval in six.iteritems(fit_params):<EOL><INDENT>step, param = pname.split('<STR_LIT>', <NUM_LIT:1>)<EOL>fit_params_steps[step][param] = pval<EOL><DEDENT>transformers = Parallel(n_jobs=self.n_jobs, backend="<STR_LIT>")(<EOL>delayed(_fit_one_transformer)(trans, Z, **fit_params_steps[name])<EOL>for name, trans in self.transformer_list)<EOL>self._update_transformer_list(transformers)<EOL>return self<EOL>
TODO: rewrite docstring
        Fit all transformers using X.
        Parameters
        ----------
        X : array-like or sparse matrix, shape (n_samples, n_features)
            Input data, used to fit transformers.