How to use the msticpy.analysis.anomalous_sequence.utils.data_structures.Cmd function in msticpy

session=[],
            prior_probs=self.data3["prior_probs"],
            trans_probs=self.data3["trans_probs"],
            param_cond_cmd_probs=self.data3["param_cond_cmd_probs"],
            value_cond_param_probs=self.data3["value_cond_param_probs"],
            modellable_params=set(),
            window_len=1,
            use_start_end_tokens=True,
            start_token=START_TOKEN,
            end_token=END_TOKEN,
            use_geo_mean=False,
        )
        self.assertListEqual(actual, [0.25])

        actual = cmds_params_values.compute_likelihood_windows_in_session(
            session=[Cmd("Set-User", {"Identity": "blah"})],
            prior_probs=self.data3["prior_probs"],
            trans_probs=self.data3["trans_probs"],
            param_cond_cmd_probs=self.data3["param_cond_cmd_probs"],
            value_cond_param_probs=self.data3["value_cond_param_probs"],
            modellable_params=set(),
            window_len=1,
            use_start_end_tokens=False,
            start_token=START_TOKEN,
            end_token=END_TOKEN,
            use_geo_mean=False,
        )
        self.assertListEqual(actual, [0.22787717886202657])

prior_probs=self.data3["prior_probs"],
            trans_probs=self.data3["trans_probs"],
            param_cond_cmd_probs=self.data3["param_cond_cmd_probs"],
            value_cond_param_probs=self.data3["value_cond_param_probs"],
            modellable_params=set(),
            window_len=1,
            use_start_end_tokens=True,
            start_token=START_TOKEN,
            end_token=END_TOKEN,
            use_geo_mean=False,
        )
        self.assertListEqual(actual[0], [])
        self.assertEqual(actual[1], 0.25)

        actual = cmds_params_values.rarest_window_session(
            session=[Cmd("Set-User", {"City": "york"}), Cmd("drfjh", {})],
            prior_probs=self.data3["prior_probs"],
            trans_probs=self.data3["trans_probs"],
            param_cond_cmd_probs=self.data3["param_cond_cmd_probs"],
            value_cond_param_probs=self.data3["value_cond_param_probs"],
            modellable_params=set(),
            window_len=3,
            use_start_end_tokens=False,
            start_token=START_TOKEN,
            end_token=END_TOKEN,
            use_geo_mean=False,
        )

        self.assertListEqual(actual[0], [])
        self.assertTrue(np.isnan(actual[1]))

        actual = cmds_params_values.rarest_window_session(

START_TOKEN: {UNK_TOKEN: 1},
            END_TOKEN: {UNK_TOKEN: 1},
            UNK_TOKEN: {UNK_TOKEN: 1},
        }
        self.data2["value_counts"] = defaultdict(lambda: 0)
        self.data2["value_counts_ls"] = {UNK_TOKEN: 1}
        self.data2["param_value_counts"] = defaultdict(lambda: defaultdict(lambda: 0))
        self.data2["param_value_counts_ls"] = {UNK_TOKEN: {UNK_TOKEN: 1}}
        self.data2["cmds"] = [START_TOKEN, END_TOKEN, UNK_TOKEN]
        self.data2["params"] = [UNK_TOKEN]

        # populate data3
        cmd = "Set-User"
        self.data3["sessions"] = [
            [
                Cmd(name="Set-User", params={"City": "york", "Identity": "blah"}),
                Cmd(name="Set-User", params={"Identity": "blah"}),
            ]
        ]
        self.data3["seq1_counts"] = defaultdict(
            lambda: 0, {START_TOKEN: 1, cmd: 2, END_TOKEN: 1}
        )
        self.data3["seq1_counts_ls"] = {
            UNK_TOKEN: 6,
            START_TOKEN: 4,
            END_TOKEN: 4,
            cmd: 8,
        }
        self.data3["seq2_counts"] = defaultdict(lambda: defaultdict(lambda: 0))
        self.data3["seq2_counts"][START_TOKEN][cmd] = 1
        self.data3["seq2_counts"][cmd][cmd] = 1
        self.data3["seq2_counts"][cmd][END_TOKEN] = 1

]
        self.sessions2 = [
            [
                Cmd("Set-User", {"Identity"}),
                Cmd("Set-User", {"Identity", "City", "Name"}),
            ],
            [
                Cmd("Set-Mailbox", {"Identity"}),
                Cmd("Set-User", {"Identity", "City"}),
                Cmd("Set-User", {"Identity"}),
            ],
        ]
        self.sessions3 = [
            [
                Cmd("Set-User", {"Identity": "blah"}),
                Cmd("Set-User", {"Identity": "haha", "City": "york", "Name": "bob"}),
            ],
            [
                Cmd("Set-Mailbox", {"Identity": "blah"}),
                Cmd("Set-User", {"Identity": "blah", "City": "london"}),
                Cmd("Set-User", {"Identity": "haha"}),
            ],
        ]
        self.times = [pd.datetime(2019, 3, 1), pd.datetime(2019, 5, 6)]
        self.data1 = pd.DataFrame({"session": self.sessions1, "time": self.times})
        self.data2 = pd.DataFrame({"session": self.sessions2, "time": self.times})
        self.data3 = pd.DataFrame({"session": self.sessions3, "time": self.times})

self.data2["param_probs"] = StateMatrix({UNK_TOKEN: 0.3}, UNK_TOKEN)
        self.data2["param_cond_cmd_probs"] = StateMatrix(
            {
                START_TOKEN: {UNK_TOKEN: 0.3333333333333333},
                END_TOKEN: {UNK_TOKEN: 0.3333333333333333},
                UNK_TOKEN: {UNK_TOKEN: 0.25},
            },
            UNK_TOKEN,
        )

        # populate data3
        cmd = "Set-User"
        self.data3["sessions"] = [
            [
                Cmd(name="Set-User", params={"City", "Identity"}),
                Cmd(name="Set-User", params={"Identity"}),
            ]
        ]
        self.data3["_seq1_counts"] = defaultdict(
            lambda: 0, {START_TOKEN: 1, END_TOKEN: 1, cmd: 2}
        )
        self.data3["seq1_counts"] = StateMatrix(
            {UNK_TOKEN: 6, START_TOKEN: 4, END_TOKEN: 4, cmd: 8}, UNK_TOKEN
        )
        self.data3["_seq2_counts"] = defaultdict(lambda: defaultdict(lambda: 0))
        self.data3["_seq2_counts"][START_TOKEN][cmd] = 1
        self.data3["_seq2_counts"][cmd][END_TOKEN] = 1
        self.data3["_seq2_counts"][cmd][cmd] = 1
        self.data3["seq2_counts"] = StateMatrix(
            {
                START_TOKEN: {END_TOKEN: 1, UNK_TOKEN: 1, cmd: 2},
                UNK_TOKEN: {END_TOKEN: 1, UNK_TOKEN: 1, cmd: 1},

def setUp(self) -> None:
        self.sessions1 = [
            ["Set-User", "Set-User"],
            ["Set-Mailbox", "Set-User", "Set-User"],
        ]
        self.sessions2 = [
            [
                Cmd("Set-User", {"Identity"}),
                Cmd("Set-User", {"Identity", "City", "Name"}),
            ],
            [
                Cmd("Set-Mailbox", {"Identity"}),
                Cmd("Set-User", {"Identity", "City"}),
                Cmd("Set-User", {"Identity"}),
            ],
        ]
        self.sessions3 = [
            [
                Cmd("Set-User", {"Identity": "blah"}),
                Cmd("Set-User", {"Identity": "haha", "City": "york", "Name": "bob"}),
            ],
            [
                Cmd("Set-Mailbox", {"Identity": "blah"}),
                Cmd("Set-User", {"Identity": "blah", "City": "london"}),
                Cmd("Set-User", {"Identity": "haha"}),
            ],

def setUp(self) -> None:
        self.sessions1 = [
            ["Set-User", "Set-User"],
            ["Set-Mailbox", "Set-User", "Set-User"],
        ]
        self.sessions2 = [
            [
                Cmd("Set-User", {"Identity"}),
                Cmd("Set-User", {"Identity", "City", "Name"}),
            ],
            [
                Cmd("Set-Mailbox", {"Identity"}),
                Cmd("Set-User", {"Identity", "City"}),
                Cmd("Set-User", {"Identity"}),
            ],
        ]
        self.sessions3 = [
            [
                Cmd("Set-User", {"Identity": "blah"}),
                Cmd("Set-User", {"Identity": "haha", "City": "york", "Name": "bob"}),
            ],
            [
                Cmd("Set-Mailbox", {"Identity": "blah"}),
                Cmd("Set-User", {"Identity": "blah", "City": "london"}),
                Cmd("Set-User", {"Identity": "haha"}),
            ],
        ]
        self.times = [pd.datetime(2019, 3, 1), pd.datetime(2019, 5, 6)]

Returns
    -------
    list of likelihoods

    """
    if use_start_end_tokens:
        if start_token is None or end_token is None:
            raise MsticpyException(
                "start_token and end_token should not be set to None when "
                "use_start_end_tokens is set to True"
            )

    likelihoods = []
    sess = session.copy()
    if use_start_end_tokens and end_token:
        sess += [Cmd(name=str(end_token), params=dict())]
    end = len(sess) - window_len
    for i in range(end + 1):
        window = sess[i : i + window_len]  # noqa E203
        if i == 0:
            use_start = use_start_end_tokens
        else:
            use_start = False
        lik = compute_likelihood_window(
            window=window,
            prior_probs=prior_probs,
            trans_probs=trans_probs,
            param_cond_cmd_probs=param_cond_cmd_probs,
            value_cond_param_probs=value_cond_param_probs,
            modellable_params=modellable_params,
            use_start_token=use_start,
            use_end_token=False,

-------
    List[float]
        list of likelihoods

    """
    if use_start_end_tokens:
        if start_token is None or end_token is None:
            raise MsticpyException(
                "start_token and end_token should not be set to None when "
                "use_start_end_tokens is set to True"
            )

    likelihoods = []
    sess = session.copy()
    if use_start_end_tokens and end_token:
        sess += [Cmd(name=str(end_token), params={})]
    end = len(sess) - window_len

    for i in range(end + 1):
        window = sess[i : i + window_len]  # noqa E203
        if i == 0:
            use_start = use_start_end_tokens
        else:
            use_start = False

        lik = compute_likelihood_window(
            window=window,
            prior_probs=prior_probs,
            trans_probs=trans_probs,
            param_cond_cmd_probs=param_cond_cmd_probs,
            use_start_token=use_start,
            use_end_token=False,