Secant method and method of false position (10 marks) Below is a simple example for the dynamics of a "neuron" in a continuous-time artificial neural network dtdx=ax+ctanh(x)+b The first term makes sure that x does not explode to intinity. The second term represents an activation function and the last is a bias term. For this assignment, use the parameter values a=0.4,b=0.25 and c=1. When dtdx=0 the neuron is at equilibium (i.e. Its dynamics does not change in time). Below you will solve this equation numericaly to find all equilibrium values of x. Provide a Python implementation of the secant method below. Let accuracy tolerance TOL and maximum number of iterations N0 be parameters of the method. Print for each case the approximated root at every iteration step