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: Perform a forward pass on the network by computing the values
  of the neurons in the hidden layers and the output layer.
  
  Parameters
  ----------
  activations: list, length = n_layers - 1
      The ith element of the list holds the values of the ith layer.
  
  with_output_activation : bool, default True
      If True, the output passes through the output activation
      function, which is either the softmax function or the
      logistic function
docstr_tok: ["Perform","a","forward","pass","on","the","network","by","computing","the","values","of","the","neurons","in","the","hidden","layers","and","the","output","layer",".","Parameters","--","--","--","--","--","activations",":","list",",","length","=","n_layers","-","1","The","ith","element","of","the","list","holds","the","values","of","the","ith","layer",".","with_output_activation",":","bool",",","default","True","If","True",",","the","output","passes","through","the","output","activation","function",",","which","is","either","the","softmax","function","or","the","logistic","function"]
code: def _forward_pass(self, activations):
      """Perform a forward pass on the network by computing the values
      of the neurons in the hidden layers and the output layer.
  
      Parameters
      ----------
      activations: list, length = n_layers - 1
          The ith element of the list holds the values of the ith layer.
  
      with_output_activation : bool, default True
          If True, the output passes through the output activation
          function, which is either the softmax function or the
          logistic function
      """
      hidden_activation = ACTIVATIONS[self.activation]
      # Iterate over the hidden layers
      for i in range(self.n_layers_ - 1):
          activations[i + 1] = safe_sparse_dot(
              activations[i], self.coefs_[i]
          )
          activations[i + 1] += self.intercepts_[i]
  
          # For the hidden layers
          if (i + 1) != (self.n_layers_ - 1):
              activations[i + 1] = hidden_activation(activations[i + 1])
  
      # For the last layer
      output_activation = ACTIVATIONS[self.out_activation_]
      activations[i + 1] = output_activation(activations[i + 1])
  
      return activations
  
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 3 for calling the BaseMultilayerPerceptron obj's underlying member method code with example usage: obj._forward_pass(activations) and returns: activations