UnboundLocalError: local variable 'dist' referenced before assignment

I am trying to train a model for supervised learning for Hidden Markov Model (HMM)and test it on a set of observations however, keep getting this error. The goal is to predict the state based on the observations. How can I fix this and how can I view the transition matrix?

The version for Pomegranate is 0.14.4 Trying this from the source: https://github.com/jmschrei/pomegranate/issues/1005

from pomegranate import *
import numpy as np
# Supervised method that calculates the transition matrix:
d1 = State(UniformDistribution.from_samples([3.243221498397177, 3.210684537495482, 3.227662201472816,
    3.286410817416738, 3.290573650708864, 3.286058136226862, 3.266480693857006]))
d2 = State(UniformDistribution.from_samples([3.449282367485096, 1.97317859465635, 1.897551432353011,
     3.454609351559659, 3.127357456033111, 1.779308337786426, 3.802891929694426, 3.359766157565077, 2.959428499979418]))
d3 = State(UniformDistribution.from_samples([1.892812118441474, 1.589353118681066, 2.09269978285637,
     2.104391496570218, 1.656771181054144]))
model = HiddenMarkovModel()
model.add_states(d1, d2, d3)
# print(model.to_json())
model.bake()
model.fit([3.2, 6.7, 10.55], labels=[1, 2, 3], algorithm='labeled')
all_pred = model.predict([2.33, 1.22, 1.4, 10.6])

Error:

 File "C:\Program Files\JetBrains\PyCharm Community Edition 2021.2\plugins\python-ce\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile
    pydev_imports.execfile(filename, global_vars, local_vars)  # execute the script
  File "C:\Program Files\JetBrains\PyCharm Community Edition 2021.2\plugins\python-ce\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "C:/Users/", line 774, in <module>
    model.bake()
  File "pomegranate/hmm.pyx", line 1047, in pomegranate.hmm.HiddenMarkovModel.bake
UnboundLocalError: local variable 'dist' referenced before assignment

from UnboundLocalError: local variable 'dist' referenced before assignment