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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 plot_lines(f, x, samples, ax=None, **kwargs):
 r""" Plot a representative set of functions to sample Additionally, if a list of log-evidences are passed, along with list of functions, and list of samples, this function plots the probability mass function for all models marginalised according to the evidences. Parameters f: function function :math:`f(x;\theta)` (or list of functions for each model) with dependent variable :math:`x`, parameterised by :math:`\theta`. x: 1D array-like `x` values to evaluate :math:`f(x;\theta)` at. samples: 2D array-like :math:`\theta` samples (or list of :math:`\theta` samples) to evaluate :math:`f(x;\theta)` at. `shape = (nsamples, npars)` ax: axes object, optional :class:`matplotlib.axes._subplots.AxesSubplot` to plot the contours onto. If unsupplied, then :func:`matplotlib.pyplot.gca()` is used to get the last axis used, or create a new one. logZ: 1D array-like, optional log-evidences of each model if multiple models are passed. Should be same length as the list `f`, and need not be normalised. Default: `numpy.ones_like(f)` weights: 1D array-like, optional sample weights (or list of weights), if desired. Should have length same as `samples.shape[0]`. Default: `numpy.ones_like(samples)` ntrim: int, optional Approximate number of samples to trim down to, if desired. Useful if the posterior is dramatically oversampled. Default: None cache: str, optional File root for saving previous calculations for re-use parallel, tqdm_args: see docstring for :func:`fgivenx.parallel.parallel_apply` kwargs: further keyword arguments Any further keyword arguments are plotting keywords that are passed to :func:`fgivenx.plot.plot_lines`. """

    logZ = kwargs.pop('logZ', None)
    weights = kwargs.pop('weights', None)
    ntrim = kwargs.pop('ntrim', None)
    cache = kwargs.pop('cache', '')
    parallel = kwargs.pop('parallel', False)
    tqdm_kwargs = kwargs.pop('tqdm_kwargs', {})

    fsamps = compute_samples(f, x, samples, logZ=logZ,
                             weights=weights, ntrim=ntrim,
                             parallel=parallel, cache=cache,
                             tqdm_kwargs=tqdm_kwargs)
    fgivenx.plot.plot_lines(x, fsamps, ax, **kwargs)