[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:class bert(nn.Module):\n    def __init__(self, bert_path):\n        super().__init__()\n        BERT = BertModel.from_pretrained(bert_path, config = BertConfig.from_pretrained(bert_path, output_hidden_states = True))\n        self.BERT = BERT\n        self.fc = nn.Sequential(nn.BatchNorm1d(self.BERT.config.hidden_size * 3),\n                                nn.Dropout(0.4),\n                                nn.Linear(self.BERT.config.hidden_size * 3, 600),\n                                nn.BatchNorm1d(600),\n                                nn.Dropout(0.4),\n                                nn.Linear(600, 600),\n                                nn.BatchNorm1d(600),\n                                nn.Dropout(0.4),\n                                nn.Linear(600,3))\n        \n    def forward(self, token, at_mask, offsets, layer):\n        out = self.BERT(token, attention_mask = at_mask)[2][layer]\n        out_lst = []\n        for j in range(out.shape[0]):\n            out_lst.append(torch.stack([torch.tensor(out[j,offsets[j,0]]),torch.tensor(out[j,offsets[j,1]]),torch.tensor(out[j,offsets[j,2]])] , dim = 0) )\n        out_lst = torch.stack([word_embedding for word_embedding in out_lst], dim = 0)\n        out = out_lst.reshape(out_lst.shape[0], -1)\n        out = self.fc(out)\n        return out\n        \ndef create_model(df_len,epoch_len):        \n    model = bert(bert_path)\n    criteria = nn.CrossEntropyLoss()\n    optimizer = AdamW(model.parameters(), eps = 1e-06, lr = 1e-4)\n    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=df_len*epoch_len)\n    return model, criteria, optimizer, scheduler'}, {'role': 'assistant', 'content': "['modelling']"}]