code: def softmax(x):
      """Softmax activation function
      :math:`\\varphi(\\mathbf{x})_j =
      \\frac{e^{\mathbf{x}_j}}{\sum_{k=1}^K e^{\mathbf{x}_k}}`
      where :math:`K` is the total number of neurons in the layer. This
      activation function gets applied row-wise.
  
      Parameters
      ----------
      x : float32
          The activation (the summed, weighted input of a neuron).
  
      Returns
      -------
      float32 where the sum of the row is 1 and each single value is in [0, 1]
          The output of the softmax function applied to the activation.
      """
      return theano.tensor.nnet.softmax(x)
  
file_path: Lasagne/lasagne/nonlinearities.py
imports: import theano.tensor
label_desc: Use image node_id 2 for calling a global function with example usage: softmax(x) and returns: theano