NameError: name 'epochs' is not defined　python エラーコード

google colaboratoty にてpythonを使い。
株価のデータ可視化の練習中に以下のコードでエラーコードが出てしまいました。
コードの位置などを再度書き直したのですが、エラーが消えないのでご教授お願いいたします。

一連の流れ。

python
1import torch
2import torch.nn as nn
3import numpy as np
4import pandas as pd 
5import matplotlib.pyplot as plt

python
1stock_data = pd.read_csv(
2    "/content/drive/MyDrive/^GSPC.csv",
3    index_col = 0,
4    parse_dates=True
5)
6
7stock_data
8↓
9stock_data.drop(
10    ["Open", "High", "Low", "Close", "Volume"],
11    axis="columns",
12    inplace=True
13)
14
15stock_data
16↓
17stock_data.plot(figsize=(12, 4))
18↓
19y = stock_data["Adj Close"].values
20y
21↓
22from sklearn.preprocessing import MinMaxScaler
23↓
24scaler = MinMaxScaler(feature_range=(-1, 1))
25scaler.fit(y.reshape(-1, 1))
26y =scaler.transform(y.reshape(-1, 1))
27y
28↓
29y = torch.FloatTensor(y).view(-1)
30y 
31↓
32test_size = 24
33
34train_seq = y[:-test_size]
35test_seq = y[-test_size:]
36↓
37plt.figure(figsize=(12, 4))
38plt.xlim(-20, len(train_seq)+20)
39plt.grid(True)
40plt.plot(train_seq)
41↓
42train_window_size = 12
43↓
44def input_data(seq, ws):
45  out = []
46  L = len(seq)
47
48  for i in range(L-ws):
49    window = seq[i:i+ws]
50    label = seq[i+ws:i+ws+1]
51    out.append((window, label))
52
53  return out
54↓
55train_data = input_data(train_seq, train_window_size)
56↓
57print("The NUmber of Training Data: ",len(train_data))
58↓
59class Model(nn.Module):
60
61  def __init__(self, input=1, h=50, output=1):
62
63    super().__init__()
64    self.hidden_size = h
65
66    self.lstm = nn.LSTM(input, h)
67    self.fc = nn.Linear(h, output)
68
69    self.hidden  = (
70        torch.zeros(1, 1, h),
71        torch.zeros(1, 1, h)
72    )
73
74
75  def forward(self, seq):
76
77    out, _=self.lstm(
78        seq.vierq(len(seq), 1, -1),
79        self.hidden
80    )
81
82    out = self.fc(
83        out.view(len(seq), -1)
84    )
85
86    return out[-1]
87↓
88torch.manual_seed(123)
89model = Model()
90criterion = nn.MSELoss()
91optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
92↓
93epochs = 10
94train_losses = []
95test_losses = []
96↓
97def run_train():
98  model.train()
99
100  for train_window, correct_label in train_data:
101    optimizer.zero_grad()
102
103    model.hidden = (
104        torch.zeros(1, 1, model.hidden_size),
105        torch.zeros(1, 1, model.hidden_size),
106
107    )
108
109    train_predicred_label = model.forward(train_window)
110    train_loss = criterion(train_predicted_label, correct_label)
111
112    train_loss.backward()
113    optimizer.step()
114
115    train_losses.append(train_loss)
116↓
117a = torch.tensor([3])
118a.item()
119↓
120def run_test():
121  model.eval()
122
123  for i in range(test_size):
124
125    test_window = torch.FloatTensor(extending_seq[-test_size:])
126
127    with torch.no_grad():
128      
129        model.hidden = (
130        torch.zeros(1, 1, model.hidden_size),
131        torch.zeros(1, 1, model.hidden_size),
132
133    )
134        
135    test_predicted_label = mode.forward(test_window)
136    extending_seq.append(test_predicted_label())
137
138    test_loss = criterion(
139        torch.FloatTensor(extending_seq[-test_size:]),
140        y[len(y)-test_size:]
141    )
142        
143    test_losses.append(test_loss)
144↓
145train_seq[-test_size:]
146↓
147train_seq[-test_size:].tolist()
148↓

python
1for epoch in range(epochs):
2
3  print()
4  print(f'Epoch: {epoch+1}')
5
6  run_train()
7
8  extending_seq = train_seq[-test_size:].tolist()
9
10  run_test()
11
12  plt.figure(figsize=(12, 4))
13  plt.xlim(-21, len(y)+20)
14  plt.grid(True)
15
16  plt.plot(
17      range(len(y)-test_size, len(y)),
18      extending_seq[-test_size:]
19  )
20
21  plt.show()

Error
1Epoch: 1
2---------------------------------------------------------------------------
3AttributeError                            Traceback (most recent call last)
4<ipython-input-85-98e8d85b09e9> in <module>()
5      4   print(f'Epoch: {epoch+1}')
6      5 
7----> 6   run_train()
8      7 
9      8   extending_seq = train_seq[-test_size:].tolist()
10
111 frames
12<ipython-input-77-b3af88443259> in forward(self, seq)
13     18 
14     19     out, _=self.lstm(
15---> 20         seq.vierq(len(seq), 1, -1),
16     21         self.hidden
17     22     )
18
19AttributeError: 'Tensor' object has no attribute 'vierq'