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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 Start(self, file_size=0, maximum_pending_files=1000, use_external_stores=False):
 """Initialize our state."""

    super(MultiGetFileLogic, self).Start()

    self.state.files_hashed = 0
    self.state.use_external_stores = use_external_stores
    self.state.file_size = file_size
    self.state.files_to_fetch = 0
    self.state.files_fetched = 0
    self.state.files_skipped = 0

    # Counter to batch up hash checking in the filestore
    self.state.files_hashed_since_check = 0

    # A dict of file trackers which are waiting to be checked by the file
    # store.  Keys are vfs urns and values are FileTrack instances.  Values are
    # copied to pending_files for download if not present in FileStore.
    self.state.pending_hashes = {}

    # A dict of file trackers currently being fetched. Keys are vfs urns and
    # values are FileTracker instances.
    self.state.pending_files = {}

    # The maximum number of files we are allowed to download concurrently.
    self.state.maximum_pending_files = maximum_pending_files

    # As pathspecs are added to the flow they are appended to this array. We
    # then simply pass their index in this array as a surrogate for the full
    # pathspec. This allows us to use integers to track pathspecs in dicts etc.
    self.state.indexed_pathspecs = []
    self.state.request_data_list = []

    # The index of the next pathspec to start. Pathspecs are added to
    # indexed_pathspecs and wait there until there are free trackers for
    # them. When the number of pending_files falls below the
    # "maximum_pending_files" count] = we increment this index and start of
    # downloading another pathspec.
    self.state.next_pathspec_to_start = 0

    # Number of blob hashes we have received but not yet scheduled for download.
    self.state.blob_hashes_pending = 0