docstr: For the forward pass of the model, we randomly choose either 4, 5
  and reuse the e parameter to compute the contribution of these orders.
  
  Since each forward pass builds a dynamic computation graph, we can use normal
  Python control-flow operators like loops or conditional statements when
  defining the forward pass of the model.
  
  Here we also see that it is perfectly safe to reuse the same parameter many
  times when defining a computational graph.
docstr_tok: ["For","the","forward","pass","of","the","model",",","we","randomly","choose","either","4",",","5","and","reuse","the","e","parameter","to","compute","the","contribution","of","these","orders",".","Since","each","forward","pass","builds","a","dynamic","computation","graph",",","we","can","use","normal","Python","control-flow","operators","like","loops","or","conditional","statements","when","defining","the","forward","pass","of","the","model",".","Here","we","also","see","that","it","is","perfectly","safe","to","reuse","the","same","parameter","many","times","when","defining","a","computational","graph","."]
code: def forward(self, x):
      """
      For the forward pass of the model, we randomly choose either 4, 5
      and reuse the e parameter to compute the contribution of these orders.
  
      Since each forward pass builds a dynamic computation graph, we can use normal
      Python control-flow operators like loops or conditional statements when
      defining the forward pass of the model.
  
      Here we also see that it is perfectly safe to reuse the same parameter many
      times when defining a computational graph.
      """
      y = self.a + self.b * x + self.c * x**2 + self.d * x**3
      for exp in range(4, random.randint(4, 6)):
          y = y + self.e * x**exp
      return y
  
file_path: BigDL/ppml/trusted-python-toolkit/examples/pytorch_test.py
imports: import random
  import torch
  import math
label_desc: Use image node_id 2 for calling the DynamicNet obj's underlying member method code with example usage: obj.forward(x) and returns: y