code: def _pack(coefs_, intercepts_):
      """Pack the parameters into a single vector."""
      return np.hstack([l.ravel() for l in coefs_ + intercepts_])
  
imports: import numpy
  from abc import ABCMeta, abstractmethod
  from scipy.optimize import fmin_l_bfgs_b
  import warnings
  from ..base import BaseEstimator, ClassifierMixin, RegressorMixin
  from ._base import ACTIVATIONS, DERIVATIVES, LOSS_FUNCTIONS
  from ._stochastic_optimizers import SGDOptimizer, AdamOptimizer
  from ..model_selection import train_test_split
  from ..externals import six
  from ..preprocessing import LabelBinarizer
  from ..utils import gen_batches, check_random_state
  from ..utils import shuffle
  from ..utils import check_array, check_X_y, column_or_1d
  from ..exceptions import ConvergenceWarning
  from ..utils.extmath import safe_sparse_dot
  from ..utils.validation import check_is_fitted
  from ..utils.multiclass import _check_partial_fit_first_call, unique_labels
  from ..utils.multiclass import type_of_target
label_desc: Use image node_id 1 for calling a global function with example usage: _pack(coefs_, intercepts_) and returns: np