MultinomialHMM: ValueError: Total count for each sample should add up to the number of trials

I am trying this example from the forum below which works for the others. But I am getting the error below for some reason. What am I missing?

Link to tutorial (from superbobry): https://github.com/hmmlearn/hmmlearn/issues/70

import numpy as np
from hmmlearn import hmm

states = ["Rainy", "Sunny"]
n_states = len(states)

observations = ["walk", "shop", "clean"]
n_observations = len(observations)

model = hmm.MultinomialHMM(n_components=n_states, init_params="")
model.startprob_ = np.array([0.6, 0.4])
model.transprob_ = np.array([
  [0.7, 0.3],
  [0.4, 0.6]
])
model.emissionprob_ = np.array([
  [0.1, 0.4, 0.5],
  [0.6, 0.3, 0.1]
])

# predict a sequence of hidden states based on visible states
bob_says = np.array([[0, 2, 1, 1, 2, 0]]).T

model = model.fit(bob_says)
logprob, alice_hears = model.decode(bob_says, algorithm="viterbi")
print("Bob says:", ", ".join(map(lambda x: observations[x], bob_says)))
print("Alice hears:", ", ".join(map(lambda x: states[x], alice_hears)))

Error:

File "<ipython-input-106-3346ba6e458a>", line 21, in <module>
    model = model.fit(bob_says)
        raise ValueError("Total count for each sample should add up to "
    ValueError: Total count for each sample should add up to the number of trials

Try changing Multinomial HMM to Categorical HMM