class_docstr: Base class for MLP classification and regression.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
  
  .. versionadded:: 0.18
docstr: Extract the coefficients and intercepts from packed_parameters.
docstr_tok: ["Extract","the","coefficients","and","intercepts","from","packed_parameters","."]
code: def _unpack(self, packed_parameters):
      """Extract the coefficients and intercepts from packed_parameters."""
      for i in range(self.n_layers_ - 1):
          start, end, shape = self._coef_indptr[i]
          self.coefs_[i] = np.reshape(
              packed_parameters[start:end], shape
          )
  
          start, end = self._intercept_indptr[i]
          self.intercepts_[i] = packed_parameters[start:end]
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from itertools import chain
  from numbers import Integral, Real
  import numpy
  import scipy.optimize
  from ..base import BaseEstimator, ClassifierMixin, RegressorMixin, _fit_context, is_classifier
  from ..exceptions import ConvergenceWarning
  from ..metrics import accuracy_score, r2_score
  from ..model_selection import train_test_split
  from ..preprocessing import LabelBinarizer
  from ..utils import _safe_indexing, check_random_state, column_or_1d, gen_batches, shuffle
  from ..utils._param_validation import Interval, Options, StrOptions
  from ..utils.extmath import safe_sparse_dot
  from ..utils.metaestimators import available_if
  from ..utils.multiclass import _check_partial_fit_first_call, type_of_target, unique_labels
  from ..utils.optimize import _check_optimize_result
  from ..utils.validation import check_is_fitted
  from ._base import ACTIVATIONS, DERIVATIVES, LOSS_FUNCTIONS
  from ._stochastic_optimizers import AdamOptimizer, SGDOptimizer
label_desc: Use image node_id 2 for calling the BaseMultilayerPerceptron obj's underlying member method code with example usage: obj._unpack(packed_parameters) without return types