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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 from_json_stat(datasets, naming='label', value='value'):
 """Decode JSON-stat formatted data into pandas.DataFrame object. Args: datasets(OrderedDict, list):
 data in JSON-stat format, previously \ deserialized to a python object by \ json.load() or json.loads(), for example.\ Both List and OrderedDict are accepted \ as inputs. naming(string, optional):
 dimension naming. Possible values: 'label' or 'id'.Defaults to 'label'. value (string, optional):
 name of the value column. Defaults to 'value'. Returns: results(list):
 list of pandas.DataFrame with imported data. """


    warnings.warn(
        "Shouldn't use this function anymore! Now use read() methods of"
        "Dataset, Collection or Dimension.",
        DeprecationWarning
    )

    check_input(naming)
    results = []
    if type(datasets) is list:
        for idx, element in enumerate(datasets):
            for dataset in element:
                js_dict = datasets[idx][dataset]
                results.append(generate_df(js_dict, naming, value))
    elif isinstance(datasets, OrderedDict) or type(datasets) is dict or \
            isinstance(datasets, Dataset):
        if 'class' in datasets:
            if datasets['class'] == 'dataset':
                js_dict = datasets
                results.append(generate_df(js_dict, naming, value))
        else:  # 1.00 bundle type
            for dataset in datasets:
                js_dict = datasets[dataset]
                results.append(generate_df(js_dict, naming, value))
    return results