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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 loadmat(file_name, mdict=None, appendmat=True, variable_names=None, marshaller_collection=None, **keywords):
 """ Loads data to a MATLAB MAT file. Reads data from the specified variables (or all) in a MATLAB MAT file. There are many different formats of MAT files. This package can only handle the HDF5 based ones (the version 7.3 and later). As SciPy's ``scipy.io.loadmat`` function can handle the earlier formats, if this function cannot read the file, it will dispatch it onto the scipy function with all the calling arguments it uses passed on. This function is modelled after the SciPy one (arguments not specific to this package have the same names, etc.). Parameters file_name : str Name of the MAT file to read from. The '.mat' extension is added on automatically if not present if `appendmat` is set to ``True``. mdict : dict, optional The dictionary to insert read variables into appendmat : bool, optional Whether to append the '.mat' extension to `file_name` if it doesn't already end in it or not. variable_names: None or sequence, optional The variable names to read from the file. ``None`` selects all. marshaller_collection : MarshallerCollection, optional Collection of marshallers from disk to use. Only applicable if not dispatching to SciPy (version 7.3 and newer files). **keywords : Additional keywords arguments to be passed onto ``scipy.io.loadmat`` if dispatching to SciPy if the file is not a version 7.3 or later format. Returns ------- dict Dictionary of all the variables read from the MAT file (name as the key, and content as the value). Raises ------ ImportError If it is not a version 7.3 .mat file and the ``scipy`` module can't be found when dispatching to SciPy. exceptions.CantReadError If reading the data can't be done. Notes ----- Writing the same data and then reading it back from disk using the HDF5 based version 7.3 format (the functions in this package) or the older format (SciPy functions) can lead to very different results. Each package supports a different set of data types and converts them to and from the same MATLAB types differently. See Also -------- savemat : Equivalent function to do writing. scipy.io.loadmat : SciPy function this one models after and dispatches to. Options reads : Function used to do the actual reading. """

    # Will first assume that it is the HDF5 based 7.3 format. If an
    # OSError occurs, then it wasn't an HDF5 file and the scipy function
    # can be tried instead.
    try:
        # Make the options with the given marshallers.
        options = Options(marshaller_collection=marshaller_collection)

        # Append .mat if it isn't on the end of the file name and we are
        # supposed to.
        if appendmat and not file_name.endswith('.mat'):
            filename = file_name + '.mat'
        else:
            filename = file_name

        # Read everything if we were instructed.

        if variable_names is None:
            data = dict()
            with h5py.File(filename, mode='r') as f:
                for k in f:
                    # Read if not group_for_references. Data that
                    # produces errors when read is dicarded (the OSError
                    # that would happen if this is not an HDF5 file
                    # would already have happened when opening the
                    # file).
                    if f[k].name != options.group_for_references:
                        try:
                            data[utilities.unescape_path(k)] = \
                                utilities.read_data(f, f, k, options)
                        except:
                            pass

        else:
            # Extract the desired fields all together and then pack them
            # into a dictionary one by one.
            values = reads(paths=variable_names, filename=filename,
                           options=options)
            data = dict()
            for i, name in enumerate(variable_names):
                data[name] = values[i]

        # Read all the variables, stuff them into mdict, and return it.
        if mdict is None:
            mdict = dict()
        for k, v in data.items():
            mdict[k] = v
        return mdict
    except OSError:
        import scipy.io
        return scipy.io.loadmat(file_name, mdict, appendmat=appendmat,
                                variable_names=variable_names,
                                **keywords)