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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 _scipy_distribution_positional_args_from_dict(distribution, params):
 """Helper function that returns positional arguments for a scipy distribution using a dict of parameters. See the `cdf()` function here https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.beta.html#Methods\ to see an example of scipy's positional arguments. This function returns the arguments specified by the \ scipy.stat.distribution.cdf() for tha distribution. Args: distribution (string):
 \ The scipy distribution name. params (dict):
 \ A dict of named parameters. Raises: AttributeError: \ If an unsupported distribution is provided. """


    params['loc'] = params.get('loc', 0)
    if 'scale' not in params:
        params['scale'] = 1

    if distribution == 'norm':
        return params['mean'], params['std_dev']
    elif distribution == 'beta':
        return params['alpha'], params['beta'], params['loc'], params['scale']
    elif distribution == 'gamma':
        return params['alpha'], params['loc'], params['scale']
    # elif distribution == 'poisson':
    #    return params['lambda'], params['loc']
    elif distribution == 'uniform':
        return params['min'], params['max']
    elif distribution == 'chi2':
        return params['df'], params['loc'], params['scale']
    elif distribution == 'expon':
        return params['loc'], params['scale']