We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
word Language Model bug
self.decoder = nn.Linear(**ninp**, ntoken) in model.py line 124 shoud be "nhid" 

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `word_language_model/model.py`
Content:
```
1 import math
2 import torch
3 import torch.nn as nn
4 import torch.nn.functional as F
5 
6 class RNNModel(nn.Module):
7     """Container module with an encoder, a recurrent module, and a decoder."""
8 
9     def __init__(self, rnn_type, ntoken, ninp, nhid, nlayers, dropout=0.5, tie_weights=False):
10         super(RNNModel, self).__init__()
11         self.ntoken = ntoken
12         self.drop = nn.Dropout(dropout)
13         self.encoder = nn.Embedding(ntoken, ninp)
14         if rnn_type in ['LSTM', 'GRU']:
15             self.rnn = getattr(nn, rnn_type)(ninp, nhid, nlayers, dropout=dropout)
16         else:
17             try:
18                 nonlinearity = {'RNN_TANH': 'tanh', 'RNN_RELU': 'relu'}[rnn_type]
19             except KeyError as e:
20                 raise ValueError( """An invalid option for `--model` was supplied,
21                                  options are ['LSTM', 'GRU', 'RNN_TANH' or 'RNN_RELU']""") from e
22             self.rnn = nn.RNN(ninp, nhid, nlayers, nonlinearity=nonlinearity, dropout=dropout)
23         self.decoder = nn.Linear(nhid, ntoken)
24 
25         # Optionally tie weights as in:
26         # "Using the Output Embedding to Improve Language Models" (Press & Wolf 2016)
27         # https://arxiv.org/abs/1608.05859
28         # and
29         # "Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling" (Inan et al. 2016)
30         # https://arxiv.org/abs/1611.01462
31         if tie_weights:
32             if nhid != ninp:
33                 raise ValueError('When using the tied flag, nhid must be equal to emsize')
34             self.decoder.weight = self.encoder.weight
35 
36         self.init_weights()
37 
38         self.rnn_type = rnn_type
39         self.nhid = nhid
40         self.nlayers = nlayers
41 
42     def init_weights(self):
43         initrange = 0.1
44         nn.init.uniform_(self.encoder.weight, -initrange, initrange)
45         nn.init.zeros_(self.decoder.bias)
46         nn.init.uniform_(self.decoder.weight, -initrange, initrange)
47 
48     def forward(self, input, hidden):
49         emb = self.drop(self.encoder(input))
50         output, hidden = self.rnn(emb, hidden)
51         output = self.drop(output)
52         decoded = self.decoder(output)
53         decoded = decoded.view(-1, self.ntoken)
54         return F.log_softmax(decoded, dim=1), hidden
55 
56     def init_hidden(self, bsz):
57         weight = next(self.parameters())
58         if self.rnn_type == 'LSTM':
59             return (weight.new_zeros(self.nlayers, bsz, self.nhid),
60                     weight.new_zeros(self.nlayers, bsz, self.nhid))
61         else:
62             return weight.new_zeros(self.nlayers, bsz, self.nhid)
63 
64 # Temporarily leave PositionalEncoding module here. Will be moved somewhere else.
65 class PositionalEncoding(nn.Module):
66     r"""Inject some information about the relative or absolute position of the tokens in the sequence.
67         The positional encodings have the same dimension as the embeddings, so that the two can be summed.
68         Here, we use sine and cosine functions of different frequencies.
69     .. math:
70         \text{PosEncoder}(pos, 2i) = sin(pos/10000^(2i/d_model))
71         \text{PosEncoder}(pos, 2i+1) = cos(pos/10000^(2i/d_model))
72         \text{where pos is the word position and i is the embed idx)
73     Args:
74         d_model: the embed dim (required).
75         dropout: the dropout value (default=0.1).
76         max_len: the max. length of the incoming sequence (default=5000).
77     Examples:
78         >>> pos_encoder = PositionalEncoding(d_model)
79     """
80 
81     def __init__(self, d_model, dropout=0.1, max_len=5000):
82         super(PositionalEncoding, self).__init__()
83         self.dropout = nn.Dropout(p=dropout)
84 
85         pe = torch.zeros(max_len, d_model)
86         position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
87         div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
88         pe[:, 0::2] = torch.sin(position * div_term)
89         pe[:, 1::2] = torch.cos(position * div_term)
90         pe = pe.unsqueeze(0).transpose(0, 1)
91         self.register_buffer('pe', pe)
92 
93     def forward(self, x):
94         r"""Inputs of forward function
95         Args:
96             x: the sequence fed to the positional encoder model (required).
97         Shape:
98             x: [sequence length, batch size, embed dim]
99             output: [sequence length, batch size, embed dim]
100         Examples:
101             >>> output = pos_encoder(x)
102         """
103 
104         x = x + self.pe[:x.size(0), :]
105         return self.dropout(x)
106 
107 class TransformerModel(nn.Module):
108     """Container module with an encoder, a recurrent or transformer module, and a decoder."""
109 
110     def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5):
111         super(TransformerModel, self).__init__()
112         try:
113             from torch.nn import TransformerEncoder, TransformerEncoderLayer
114         except BaseException as e:
115             raise ImportError('TransformerEncoder module does not exist in PyTorch 1.1 or '
116                               'lower.') from e
117         self.model_type = 'Transformer'
118         self.src_mask = None
119         self.pos_encoder = PositionalEncoding(ninp, dropout)
120         encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout)
121         self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
122         self.encoder = nn.Embedding(ntoken, ninp)
123         self.ninp = ninp
124         self.decoder = nn.Linear(ninp, ntoken)
125 
126         self.init_weights()
127 
128     def _generate_square_subsequent_mask(self, sz):
129         mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
130         mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
131         return mask
132 
133     def init_weights(self):
134         initrange = 0.1
135         nn.init.uniform_(self.encoder.weight, -initrange, initrange)
136         nn.init.zeros_(self.decoder.bias)
137         nn.init.uniform_(self.decoder.weight, -initrange, initrange)
138 
139     def forward(self, src, has_mask=True):
140         if has_mask:
141             device = src.device
142             if self.src_mask is None or self.src_mask.size(0) != len(src):
143                 mask = self._generate_square_subsequent_mask(len(src)).to(device)
144                 self.src_mask = mask
145         else:
146             self.src_mask = None
147 
148         src = self.encoder(src) * math.sqrt(self.ninp)
149         src = self.pos_encoder(src)
150         output = self.transformer_encoder(src, self.src_mask)
151         output = self.decoder(output)
152         return F.log_softmax(output, dim=-1)
153
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/word_language_model/model.py b/word_language_model/model.py
--- a/word_language_model/model.py
+++ b/word_language_model/model.py
@@ -121,7 +121,7 @@
         self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
         self.encoder = nn.Embedding(ntoken, ninp)
         self.ninp = ninp
-        self.decoder = nn.Linear(ninp, ntoken)
+        self.decoder = nn.Linear(nhid, ntoken)
 
         self.init_weights()

{"golden_diff": "diff --git a/word_language_model/model.py b/word_language_model/model.py\n--- a/word_language_model/model.py\n+++ b/word_language_model/model.py\n@@ -121,7 +121,7 @@\n         self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)\n         self.encoder = nn.Embedding(ntoken, ninp)\n         self.ninp = ninp\n-        self.decoder = nn.Linear(ninp, ntoken)\n+        self.decoder = nn.Linear(nhid, ntoken)\n \n         self.init_weights()\n", "issue": "word Language Model bug\nself.decoder = nn.Linear(**ninp**, ntoken) in model.py line 124 shoud be \"nhid\" \n", "before_files": [{"content": "import math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass RNNModel(nn.Module):\n    \"\"\"Container module with an encoder, a recurrent module, and a decoder.\"\"\"\n\n    def __init__(self, rnn_type, ntoken, ninp, nhid, nlayers, dropout=0.5, tie_weights=False):\n        super(RNNModel, self).__init__()\n        self.ntoken = ntoken\n        self.drop = nn.Dropout(dropout)\n        self.encoder = nn.Embedding(ntoken, ninp)\n        if rnn_type in ['LSTM', 'GRU']:\n            self.rnn = getattr(nn, rnn_type)(ninp, nhid, nlayers, dropout=dropout)\n        else:\n            try:\n                nonlinearity = {'RNN_TANH': 'tanh', 'RNN_RELU': 'relu'}[rnn_type]\n            except KeyError as e:\n                raise ValueError( \"\"\"An invalid option for `--model` was supplied,\n                                 options are ['LSTM', 'GRU', 'RNN_TANH' or 'RNN_RELU']\"\"\") from e\n            self.rnn = nn.RNN(ninp, nhid, nlayers, nonlinearity=nonlinearity, dropout=dropout)\n        self.decoder = nn.Linear(nhid, ntoken)\n\n        # Optionally tie weights as in:\n        # \"Using the Output Embedding to Improve Language Models\" (Press & Wolf 2016)\n        # https://arxiv.org/abs/1608.05859\n        # and\n        # \"Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling\" (Inan et al. 2016)\n        # https://arxiv.org/abs/1611.01462\n        if tie_weights:\n            if nhid != ninp:\n                raise ValueError('When using the tied flag, nhid must be equal to emsize')\n            self.decoder.weight = self.encoder.weight\n\n        self.init_weights()\n\n        self.rnn_type = rnn_type\n        self.nhid = nhid\n        self.nlayers = nlayers\n\n    def init_weights(self):\n        initrange = 0.1\n        nn.init.uniform_(self.encoder.weight, -initrange, initrange)\n        nn.init.zeros_(self.decoder.bias)\n        nn.init.uniform_(self.decoder.weight, -initrange, initrange)\n\n    def forward(self, input, hidden):\n        emb = self.drop(self.encoder(input))\n        output, hidden = self.rnn(emb, hidden)\n        output = self.drop(output)\n        decoded = self.decoder(output)\n        decoded = decoded.view(-1, self.ntoken)\n        return F.log_softmax(decoded, dim=1), hidden\n\n    def init_hidden(self, bsz):\n        weight = next(self.parameters())\n        if self.rnn_type == 'LSTM':\n            return (weight.new_zeros(self.nlayers, bsz, self.nhid),\n                    weight.new_zeros(self.nlayers, bsz, self.nhid))\n        else:\n            return weight.new_zeros(self.nlayers, bsz, self.nhid)\n\n# Temporarily leave PositionalEncoding module here. Will be moved somewhere else.\nclass PositionalEncoding(nn.Module):\n    r\"\"\"Inject some information about the relative or absolute position of the tokens in the sequence.\n        The positional encodings have the same dimension as the embeddings, so that the two can be summed.\n        Here, we use sine and cosine functions of different frequencies.\n    .. math:\n        \\text{PosEncoder}(pos, 2i) = sin(pos/10000^(2i/d_model))\n        \\text{PosEncoder}(pos, 2i+1) = cos(pos/10000^(2i/d_model))\n        \\text{where pos is the word position and i is the embed idx)\n    Args:\n        d_model: the embed dim (required).\n        dropout: the dropout value (default=0.1).\n        max_len: the max. length of the incoming sequence (default=5000).\n    Examples:\n        >>> pos_encoder = PositionalEncoding(d_model)\n    \"\"\"\n\n    def __init__(self, d_model, dropout=0.1, max_len=5000):\n        super(PositionalEncoding, self).__init__()\n        self.dropout = nn.Dropout(p=dropout)\n\n        pe = torch.zeros(max_len, d_model)\n        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))\n        pe[:, 0::2] = torch.sin(position * div_term)\n        pe[:, 1::2] = torch.cos(position * div_term)\n        pe = pe.unsqueeze(0).transpose(0, 1)\n        self.register_buffer('pe', pe)\n\n    def forward(self, x):\n        r\"\"\"Inputs of forward function\n        Args:\n            x: the sequence fed to the positional encoder model (required).\n        Shape:\n            x: [sequence length, batch size, embed dim]\n            output: [sequence length, batch size, embed dim]\n        Examples:\n            >>> output = pos_encoder(x)\n        \"\"\"\n\n        x = x + self.pe[:x.size(0), :]\n        return self.dropout(x)\n\nclass TransformerModel(nn.Module):\n    \"\"\"Container module with an encoder, a recurrent or transformer module, and a decoder.\"\"\"\n\n    def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5):\n        super(TransformerModel, self).__init__()\n        try:\n            from torch.nn import TransformerEncoder, TransformerEncoderLayer\n        except BaseException as e:\n            raise ImportError('TransformerEncoder module does not exist in PyTorch 1.1 or '\n                              'lower.') from e\n        self.model_type = 'Transformer'\n        self.src_mask = None\n        self.pos_encoder = PositionalEncoding(ninp, dropout)\n        encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout)\n        self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)\n        self.encoder = nn.Embedding(ntoken, ninp)\n        self.ninp = ninp\n        self.decoder = nn.Linear(ninp, ntoken)\n\n        self.init_weights()\n\n    def _generate_square_subsequent_mask(self, sz):\n        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)\n        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))\n        return mask\n\n    def init_weights(self):\n        initrange = 0.1\n        nn.init.uniform_(self.encoder.weight, -initrange, initrange)\n        nn.init.zeros_(self.decoder.bias)\n        nn.init.uniform_(self.decoder.weight, -initrange, initrange)\n\n    def forward(self, src, has_mask=True):\n        if has_mask:\n            device = src.device\n            if self.src_mask is None or self.src_mask.size(0) != len(src):\n                mask = self._generate_square_subsequent_mask(len(src)).to(device)\n                self.src_mask = mask\n        else:\n            self.src_mask = None\n\n        src = self.encoder(src) * math.sqrt(self.ninp)\n        src = self.pos_encoder(src)\n        output = self.transformer_encoder(src, self.src_mask)\n        output = self.decoder(output)\n        return F.log_softmax(output, dim=-1)\n", "path": "word_language_model/model.py"}], "after_files": [{"content": "import math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass RNNModel(nn.Module):\n    \"\"\"Container module with an encoder, a recurrent module, and a decoder.\"\"\"\n\n    def __init__(self, rnn_type, ntoken, ninp, nhid, nlayers, dropout=0.5, tie_weights=False):\n        super(RNNModel, self).__init__()\n        self.ntoken = ntoken\n        self.drop = nn.Dropout(dropout)\n        self.encoder = nn.Embedding(ntoken, ninp)\n        if rnn_type in ['LSTM', 'GRU']:\n            self.rnn = getattr(nn, rnn_type)(ninp, nhid, nlayers, dropout=dropout)\n        else:\n            try:\n                nonlinearity = {'RNN_TANH': 'tanh', 'RNN_RELU': 'relu'}[rnn_type]\n            except KeyError as e:\n                raise ValueError( \"\"\"An invalid option for `--model` was supplied,\n                                 options are ['LSTM', 'GRU', 'RNN_TANH' or 'RNN_RELU']\"\"\") from e\n            self.rnn = nn.RNN(ninp, nhid, nlayers, nonlinearity=nonlinearity, dropout=dropout)\n        self.decoder = nn.Linear(nhid, ntoken)\n\n        # Optionally tie weights as in:\n        # \"Using the Output Embedding to Improve Language Models\" (Press & Wolf 2016)\n        # https://arxiv.org/abs/1608.05859\n        # and\n        # \"Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling\" (Inan et al. 2016)\n        # https://arxiv.org/abs/1611.01462\n        if tie_weights:\n            if nhid != ninp:\n                raise ValueError('When using the tied flag, nhid must be equal to emsize')\n            self.decoder.weight = self.encoder.weight\n\n        self.init_weights()\n\n        self.rnn_type = rnn_type\n        self.nhid = nhid\n        self.nlayers = nlayers\n\n    def init_weights(self):\n        initrange = 0.1\n        nn.init.uniform_(self.encoder.weight, -initrange, initrange)\n        nn.init.zeros_(self.decoder.bias)\n        nn.init.uniform_(self.decoder.weight, -initrange, initrange)\n\n    def forward(self, input, hidden):\n        emb = self.drop(self.encoder(input))\n        output, hidden = self.rnn(emb, hidden)\n        output = self.drop(output)\n        decoded = self.decoder(output)\n        decoded = decoded.view(-1, self.ntoken)\n        return F.log_softmax(decoded, dim=1), hidden\n\n    def init_hidden(self, bsz):\n        weight = next(self.parameters())\n        if self.rnn_type == 'LSTM':\n            return (weight.new_zeros(self.nlayers, bsz, self.nhid),\n                    weight.new_zeros(self.nlayers, bsz, self.nhid))\n        else:\n            return weight.new_zeros(self.nlayers, bsz, self.nhid)\n\n# Temporarily leave PositionalEncoding module here. Will be moved somewhere else.\nclass PositionalEncoding(nn.Module):\n    r\"\"\"Inject some information about the relative or absolute position of the tokens in the sequence.\n        The positional encodings have the same dimension as the embeddings, so that the two can be summed.\n        Here, we use sine and cosine functions of different frequencies.\n    .. math:\n        \\text{PosEncoder}(pos, 2i) = sin(pos/10000^(2i/d_model))\n        \\text{PosEncoder}(pos, 2i+1) = cos(pos/10000^(2i/d_model))\n        \\text{where pos is the word position and i is the embed idx)\n    Args:\n        d_model: the embed dim (required).\n        dropout: the dropout value (default=0.1).\n        max_len: the max. length of the incoming sequence (default=5000).\n    Examples:\n        >>> pos_encoder = PositionalEncoding(d_model)\n    \"\"\"\n\n    def __init__(self, d_model, dropout=0.1, max_len=5000):\n        super(PositionalEncoding, self).__init__()\n        self.dropout = nn.Dropout(p=dropout)\n\n        pe = torch.zeros(max_len, d_model)\n        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))\n        pe[:, 0::2] = torch.sin(position * div_term)\n        pe[:, 1::2] = torch.cos(position * div_term)\n        pe = pe.unsqueeze(0).transpose(0, 1)\n        self.register_buffer('pe', pe)\n\n    def forward(self, x):\n        r\"\"\"Inputs of forward function\n        Args:\n            x: the sequence fed to the positional encoder model (required).\n        Shape:\n            x: [sequence length, batch size, embed dim]\n            output: [sequence length, batch size, embed dim]\n        Examples:\n            >>> output = pos_encoder(x)\n        \"\"\"\n\n        x = x + self.pe[:x.size(0), :]\n        return self.dropout(x)\n\nclass TransformerModel(nn.Module):\n    \"\"\"Container module with an encoder, a recurrent or transformer module, and a decoder.\"\"\"\n\n    def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5):\n        super(TransformerModel, self).__init__()\n        try:\n            from torch.nn import TransformerEncoder, TransformerEncoderLayer\n        except BaseException as e:\n            raise ImportError('TransformerEncoder module does not exist in PyTorch 1.1 or '\n                              'lower.') from e\n        self.model_type = 'Transformer'\n        self.src_mask = None\n        self.pos_encoder = PositionalEncoding(ninp, dropout)\n        encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout)\n        self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)\n        self.encoder = nn.Embedding(ntoken, ninp)\n        self.ninp = ninp\n        self.decoder = nn.Linear(nhid, ntoken)\n\n        self.init_weights()\n\n    def _generate_square_subsequent_mask(self, sz):\n        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)\n        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))\n        return mask\n\n    def init_weights(self):\n        initrange = 0.1\n        nn.init.uniform_(self.encoder.weight, -initrange, initrange)\n        nn.init.zeros_(self.decoder.bias)\n        nn.init.uniform_(self.decoder.weight, -initrange, initrange)\n\n    def forward(self, src, has_mask=True):\n        if has_mask:\n            device = src.device\n            if self.src_mask is None or self.src_mask.size(0) != len(src):\n                mask = self._generate_square_subsequent_mask(len(src)).to(device)\n                self.src_mask = mask\n        else:\n            self.src_mask = None\n\n        src = self.encoder(src) * math.sqrt(self.ninp)\n        src = self.pos_encoder(src)\n        output = self.transformer_encoder(src, self.src_mask)\n        output = self.decoder(output)\n        return F.log_softmax(output, dim=-1)\n", "path": "word_language_model/model.py"}]}