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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def tent_map(x, steps, mu=2):
 """ Generates a time series of the tent map. Characteristics and Background: The name of the tent map is derived from the fact that the plot of x_i vs x_i+1 looks like a tent. For mu > 1 one application of the mapping function can be viewed as stretching the surface on which the value is located and then folding the area that is greater than one back towards the zero. This corresponds nicely to the definition of chaos as expansion in one dimension which is counteracted by a compression in another dimension. Calculating the Lyapunov exponent: The lyapunov exponent of the tent map can be easily calculated as due to this stretching behavior a small difference delta between two neighboring points will indeed grow exponentially by a factor of mu in each iteration. We thus can assume that: delta_n = delta_0 * mu^n We now only have to change the basis to e to obtain the exact formula that is used for the definition of the lyapunov exponent: delta_n = delta_0 * e^(ln(mu) * n) Therefore the lyapunov exponent of the tent map is: lambda = ln(mu) References: .. [tm_1] https://en.wikipedia.org/wiki/Tent_map Args: x (float):
 starting point steps (int):
 number of steps for which the generator should run Kwargs: mu (int):
 parameter mu that controls the behavior of the map Returns: generator object: the generator that creates the time series """

  for _ in range(steps):
    x = mu * x if x < 0.5 else mu * (1 - x)
    yield x