class_docstr: Base class for MLP classification and regression.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
  
  .. versionadded:: 0.18
code: def _init_coef(self, fan_in, fan_out):
      if self.activation == "logistic":
          # Use the initialization method recommended by
          # Glorot et al.
          init_bound = np.sqrt(2.0 / (fan_in + fan_out))
      elif self.activation in ("identity", "tanh", "relu"):
          init_bound = np.sqrt(6.0 / (fan_in + fan_out))
      else:
          # this was caught earlier, just to make sure
          raise ValueError(
              "Unknown activation function %s" % self.activation
          )
  
      coef_init = self._random_state.uniform(
          -init_bound, init_bound, (fan_in, fan_out)
      )
      intercept_init = self._random_state.uniform(
          -init_bound, init_bound, fan_out
      )
      return coef_init, intercept_init
  
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 8 for calling the BaseMultilayerPerceptron obj's underlying member method code with example usage: obj._init_coef(fan_in, fan_out) and returns: coef_init,  intercept_init