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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 compute_acl(cls, filename, start_index=None, end_index=None, min_nsamples=10):
 """Computes the autocorrleation length for all model params in the given file. Parameter values are averaged over all walkers at each iteration. The ACL is then calculated over the averaged chain. If an ACL cannot be calculated because there are not enough samples, it will be set to ``inf``. Parameters filename : str Name of a samples file to compute ACLs for. start_index : int, optional The start index to compute the acl from. If None, will try to use the number of burn-in iterations in the file; otherwise, will start at the first sample. end_index : int, optional The end index to compute the acl to. If None, will go to the end of the current iteration. min_nsamples : int, optional Require a minimum number of samples to compute an ACL. If the number of samples per walker is less than this, will just set to ``inf``. Default is 10. Returns ------- dict A dictionary giving the ACL for each parameter. """

        acls = {}
        with cls._io(filename, 'r') as fp:
            for param in fp.variable_params:
                samples = fp.read_raw_samples(
                    param, thin_start=start_index, thin_interval=1,
                    thin_end=end_index, flatten=False)[param]
                samples = samples.mean(axis=0)
                # if < min number of samples, just set to inf
                if samples.size < min_nsamples:
                    acl = numpy.inf
                else:
                    acl = autocorrelation.calculate_acl(samples)
                if acl <= 0:
                    acl = numpy.inf
                acls[param] = acl
        return acls