[WinError 126] とはどのようなエラーでしょうか？

前提・実現したいこと

predict.pyという名前のファイルでtensorflowを用いた予測メゾッドを実行させるコードを書いています．実行させるとモジュールに関するエラーが発生しました．

発生している問題・エラーメッセージ

Trying to restore last checkpoint ...
./data/tensorflow_sessions/myo_armband/-48000
INFO:tensorflow:Restoring parameters from ./data/tensorflow_sessions/myo_armband/-48000
Failed to restore checkpoint. Initializing variables instead.
Traceback (most recent call last):

File "C:\Users\名前\Desktop\myo-python-1.0.4\myo-armband-nn-master\タイトル無し9.py", line 69, in <module>
myo.init()

File "C:\Users\名前\anaconda3\envs\Myo-ANN\lib\site-packages\myo\lowlevel\ctyping.py", line 119, in init
self._lib = ctypes.cdll.LoadLibrary(lib_name)

File "C:\Users\名前\anaconda3\envs\Myo-ANN\lib\ctypes_init_.py", line 442, in LoadLibrary
return self._dlltype(name)

File "C:\Users\名前\anaconda3\envs\Myo-ANN\lib\ctypes_init_.py", line 364, in init
self._handle = _dlopen(self._name, mode)
[WinError 126] 指定されたモジュールが見つかりません。

### 該当のソースコード

from
1#import collections
2import myo
3from threading import Lock
4import time
5import numpy as np
6import tensorflow as tf
7from include.model import model
8
9
10x, y, output, global_step, y_pred_cls = model()
11
12saver = tf.train.Saver()
13_SAVE_PATH = "./data/tensorflow_sessions/myo_armband/"
14sess = tf.Session()
15
16
17try:
18    print("Trying to restore last checkpoint ...")
19    last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=_SAVE_PATH)
20    print(last_chk_path)
21    saver.restore(sess, save_path=last_chk_path)
22    print("Restored checkpoint from:", last_chk_path)
23except:
24    print("Failed to restore checkpoint. Initializing variables instead.")
25    sess.run(tf.global_variables_initializer())
26
27
28class ApiDeviceListener(myo.DeviceListener):
29
30    def __init__(self, queue_size=8):
31        #self.lock = threading.Lock()
32        self.lock = Lock()
33        #self.emg_data_queue = collections.deque(maxlen=queue_size)
34        self.emg_data_queue = deque(maxlen=queue_size)
35        self.orientation_data_queue = deque(maxlen=queue_size)
36        self.acceleration_data_queue = deque(maxlen=queue_size)
37        self.gyroscope_data_queue = deque(maxlen=queue_size)
38        self.rssi_data_queue = deque(maxlen=100)
39    def on_connect(self, device, timestamp, firmware_version, event):
40        device.set_stream_emg(myo.StreamEmg.enabled)
41        self.device = event.device  # sauvegarde de l'instance au myo arm
42        event.device.unlock()  # demande de desappareiller
43        event.device.lock()  # demande d'appareiller (génère des vibrations)
44        event.device.stream_emg(True)  # lance l'acquisition des emg
45        self.connected = True  # mise à jour du flag de connection du myo
46        self.device_name = event.device_name  # on récupère le petit nom du myo
47        # on récupère également le numéro du firmware (non exploité dans l'UI)
48        self.myo_firmware = '.'.join(map(str, event.firmware_version[:-1]))
49    def on_emg_data(self, device, timestamp, emg_data, event):
50        with self.lock:
51            self.emg_data_queue.append((timestamp, emg_data))
52        with self.lock:
53            self.emg_data_queue.append((event.timestamp,
54                                        event.emg))
55    def get_emg_data(self):
56        with self.lock:
57            return list(self.emg_data_queue)
58        with self.lock:
59            return list(self.emg_data_queue)
60
61
62myo.init() ☚　ここではないかと
63hub = myo.Hub()
64start = time.time()
65listener = ApiDeviceListener()
66temp = []
67    
68try:
69    listener = ApiDeviceListener()
70    hub.run(listener, 2000)
71    while True:
72        data = listener.get_emg_data()
73        if time.time() - start >= 1:
74            response = np.argmax(np.bincount(temp))
75            print("Predicted gesture: {0}".format(response))
76            temp = []
77            start = time.time()
78        if len(data) > 0:
79            tmp = []
80            for v in listener.get_emg_data():
81                tmp.append(v[1])
82            tmp = list(np.stack(tmp).flatten())
83            if len(tmp) >= 64:
84                pred = sess.run(y_pred_cls, feed_dict={x: np.array([tmp])})
85                temp.append(pred[0])
86        time.sleep(0.01)
87finally:
88    #hub.shutdown()
89    sess.close()