class_docstr: The dense layer is defined by:
  
  .. math::
  
      \begin{align*}
          y_j = \sigma\left(\sum\limits_{i=0}^{F_{in}-1}w_{ij}x_i+b_j\right), && \forall 0\le j<F_{out}
      \end{align*}
  
  Parameters
  ----------
  input_size : tuple
      the size of the input.
  output_size : tuple
      the size of the output.
  weight : matrix-like
      the weight matrix.
  biases : array-like
      the biases.
  activation : str or None
      activation function name
  input_index_mapper : IndexMapper or None
      map indexes from this layer index to the input layer index size
docstr: Return the vector of node biases
docstr_tok: ["Return","the","vector","of","node","biases"]
code: def biases(self):
      """Return the vector of node biases"""
      return self.__biases
  
imports: import itertools
  import numpy
label_desc: Use image node_id 3 for calling the DenseLayer obj's underlying member method code with example usage: obj.biases() and returns: self