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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 create_db(data, dbfn, id_spec=None, force=False, verbose=False, checklines=10, merge_strategy='error', transform=None, gtf_transcript_key='transcript_id', gtf_gene_key='gene_id', gtf_subfeature='exon', force_gff=False, force_dialect_check=False, from_string=False, keep_order=False, text_factory=sqlite3.OptimizedUnicode, force_merge_fields=None, pragmas=constants.default_pragmas, sort_attribute_values=False, dialect=None, _keep_tempfiles=False, infer_gene_extent=True, disable_infer_genes=False, disable_infer_transcripts=False, **kwargs):
 """ Create a database from a GFF or GTF file. For more details on when and how to use the kwargs below, see the examples in the online documentation (:ref:`examples`). Parameters data : string or iterable If a string (and `from_string` is False), then `data` is the path to the original GFF or GTF file. If a string and `from_string` is True, then assume `data` is the actual data to use. Otherwise, it's an iterable of Feature objects. dbfn : string Path to the database that will be created. Can be the special string ":memory:" to create an in-memory database. id_spec : string, list, dict, callable, or None This parameter guides what will be used as the primary key for the database, which in turn determines how you will access individual features by name from the database. If `id_spec=None`, then auto-increment primary keys based on the feature type (e.g., "gene_1", "gene_2"). This is also the fallback behavior for the other values below. If `id_spec` is a string, then look for this key in the attributes. If it exists, then use its value as the primary key, otherwise autoincrement based on the feature type. For many GFF3 files, "ID" usually works well. If `id_spec` is a list or tuple of keys, then check for each one in order, using the first one found. For GFF3, this might be ["ID", "Name"], which would use the ID if it exists, otherwise the Name, otherwise autoincrement based on the feature type. If `id_spec` is a dictionary, then it is a mapping of feature types to what should be used as the ID. For example, for GTF files, `{'gene': 'gene_id', 'transcript': 'transcript_id'}` may be useful. The values of this dictionary can also be a list, e.g., `{'gene': ['gene_id', 'geneID']}` If `id_spec` is a callable object, then it accepts a dictionary from the iterator and returns one of the following: * None (in which case the feature type will be auto-incremented) * string (which will be used as the primary key) * special string starting with "autoincrement:X", where "X" is a string that will be used for auto-incrementing. For example, if "autoincrement:chr10", then the first feature will be "chr10_1", the second "chr10_2", and so on. force : bool If `False` (default), then raise an exception if `dbfn` already exists. Use `force=True` to overwrite any existing databases. verbose : bool Report percent complete and other feedback on how the db creation is progressing. In order to report percent complete, the entire file needs to be read once to see how many items there are; for large files you may want to use `verbose=False` to avoid this. checklines : int Number of lines to check the dialect. merge_strategy : str One of {merge, create_unique, error, warning, replace}. This parameter specifies the behavior when two items have an identical primary key. Using `merge_strategy="merge"`, then there will be a single entry in the database, but the attributes of all features with the same primary key will be merged. Using `merge_strategy="create_unique"`, then the first entry will use the original primary key, but the second entry will have a unique, autoincremented primary key assigned to it Using `merge_strategy="error"`, a :class:`gffutils.DuplicateID` exception will be raised. This means you will have to edit the file yourself to fix the duplicated IDs. Using `merge_strategy="warning"`, a warning will be printed to the logger, and the duplicate feature will be skipped. Using `merge_strategy="replace"` will replace the entire existing feature with the new feature. transform : callable Function (or other callable object) that accepts a `Feature` object and returns a (possibly modified) `Feature` object. gtf_transcript_key, gtf_gene_key : string Which attribute to use as the transcript ID and gene ID respectively for GTF files. Default is `transcript_id` and `gene_id` according to the GTF spec. gtf_subfeature : string Feature type to use as a "gene component" when inferring gene and transcript extents for GTF files. Default is `exon` according to the GTF spec. force_gff : bool If True, do not do automatic format detection -- only use GFF. force_dialect_check : bool If True, the dialect will be checkef for every feature (instead of just `checklines` features). This can be slow, but may be necessary for inconsistently-formatted input files. from_string : bool If True, then treat `data` as actual data (rather than the path to a file). keep_order : bool If True, all features returned from this instance will have the order of their attributes maintained. This can be turned on or off database-wide by setting the `keep_order` attribute or with this kwarg, or on a feature-by-feature basis by setting the `keep_order` attribute of an individual feature. Note that a single order of attributes will be used for all features. Specifically, the order will be determined by the order of attribute keys in the first `checklines` of the input data. See helpers._choose_dialect for more information on this. Default is False, since this includes a sorting step that can get time-consuming for many features. infer_gene_extent : bool DEPRECATED in version 0.8.4. See `disable_infer_transcripts` and `disable_infer_genes` for more granular control. disable_infer_transcripts, disable_infer_genes : bool Only used for GTF files. By default -- and according to the GTF spec -- we assume that there are no transcript or gene features in the file. gffutils then infers the extent of each transcript based on its constituent exons and infers the extent of each gene bases on its constituent transcripts. This default behavior is problematic if the input file already contains transcript or gene features (like recent GENCODE GTF files for human), since 1) the work to infer extents is unnecessary, and 2) trying to insert an inferred feature back into the database triggers gffutils' feature-merging routines, which can get time consuming. The solution is to use `disable_infer_transcripts=True` if your GTF already has transcripts in it, and/or `disable_infer_genes=True` if it already has genes in it. This can result in dramatic (100x) speedup. Prior to version 0.8.4, setting `infer_gene_extents=False` would disable both transcript and gene inference simultaneously. As of version 0.8.4, these argument allow more granular control. force_merge_fields : list If merge_strategy="merge", then features will only be merged if their non-attribute values are identical (same chrom, source, start, stop, score, strand, phase). Using `force_merge_fields`, you can override this behavior to allow merges even when fields are different. This list can contain one or more of ['seqid', 'source', 'featuretype', 'score', 'strand', 'frame']. The resulting merged fields will be strings of comma-separated values. Note that 'start' and 'end' are not available, since these fields need to be integers. text_factory : callable Text factory to use for the sqlite3 database. See https://docs.python.org/2/library/\ sqlite3.html#sqlite3.Connection.text_factory for details. The default sqlite3.OptimizedUnicode will return Unicode objects only for non-ASCII data, and bytestrings otherwise. pragmas : dict Dictionary of pragmas used when creating the sqlite3 database. See http://www.sqlite.org/pragma.html for a list of available pragmas. The defaults are stored in constants.default_pragmas, which can be used as a template for supplying a custom dictionary. sort_attribute_values : bool All features returned from the database will have their attribute values sorted. Typically this is only useful for testing, since this can get time-consuming for large numbers of features. _keep_tempfiles : bool or string False by default to clean up intermediate tempfiles created during GTF import. If True, then keep these tempfile for testing or debugging. If string, then keep the tempfile for testing, but also use the string as the suffix fo the tempfile. This can be useful for testing in parallel environments. Returns ------- New :class:`FeatureDB` object. """

    _locals = locals()

    # Check if any older kwargs made it in
    deprecation_handler(kwargs)

    kwargs = dict((i, _locals[i]) for i in constants._iterator_kwargs)

    # First construct an iterator so that we can identify the file format.
    # DataIterator figures out what kind of data was provided (string of lines,
    # filename, or iterable of Features) and checks `checklines` lines to
    # identify the dialect.
    iterator = iterators.DataIterator(**kwargs)

    kwargs.update(**_locals)

    if dialect is None:
        dialect = iterator.dialect

    # However, a side-effect of this is that  if `data` was a generator, then
    # we've just consumed `checklines` items (see
    # iterators.BaseIterator.__init__, which calls iterators.peek).
    #
    # But it also chains those consumed items back onto the beginning, and the
    # result is available as as iterator._iter.
    #
    # That's what we should be using now for `data:
    kwargs['data'] = iterator._iter
    kwargs['directives'] = iterator.directives

    # Since we've already checked lines, we don't want to do it again
    kwargs['checklines'] = 0

    if force_gff or (dialect['fmt'] == 'gff3'):
        cls = _GFFDBCreator
        id_spec = id_spec or 'ID'
        add_kwargs = dict(
            id_spec=id_spec,
        )

    elif dialect['fmt'] == 'gtf':
        cls = _GTFDBCreator
        id_spec = id_spec or {'gene': 'gene_id', 'transcript': 'transcript_id'}
        add_kwargs = dict(
            transcript_key=gtf_transcript_key,
            gene_key=gtf_gene_key,
            subfeature=gtf_subfeature,
            id_spec=id_spec,
        )

    kwargs.update(**add_kwargs)
    kwargs['dialect'] = dialect
    c = cls(**kwargs)

    c.create()
    if dbfn == ':memory:':
        db = interface.FeatureDB(c.conn,
                                 keep_order=keep_order,
                                 pragmas=pragmas,
                                 sort_attribute_values=sort_attribute_values,
                                 text_factory=text_factory)
    else:
        db = interface.FeatureDB(c,
                                 keep_order=keep_order,
                                 pragmas=pragmas,
                                 sort_attribute_values=sort_attribute_values,
                                 text_factory=text_factory)

    return db