[{'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:def seq2seq_model(input_data, target_data, keep_prob, batch_size, sequence_length, source_vocab_size, target_vocab_size,\nenc_embedding_size, dec_embedding_size, rnn_size, num_layers, target_vocab_to_int):\n"""\nBuild the Sequence-to-Sequence part of the neural network\n:param input_data: Input placeholder\n:param target_data: Target placeholder\n:param keep_prob: Dropout keep probability placeholder\n:param batch_size: Batch Size\n:param sequence_length: Sequence Length\n:param source_vocab_size: Source vocabulary size\n:param target_vocab_size: Target vocabulary size\n:param enc_embedding_size: Decoder embedding size\n:param dec_embedding_size: Encoder embedding size\n:param rnn_size: RNN Size\n:param num_layers: Number of layers\n:param target_vocab_to_int: Dictionary to go from the target words to an id\n:return: Tuple of (Training Logits, Inference Logits)\n"""\nenc_embed_input = tf.contrib.layers.embed_sequence(input_data, source_vocab_size, enc_embedding_size)\nencoder_state = encoding_layer(enc_embed_input, rnn_size, num_layers, keep_prob)\ntarget_data = process_decoding_input(target_data, target_vocab_to_int, batch_size)\ndec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, dec_embedding_size]))\ndec_embed_input = tf.nn.embedding_lookup(dec_embeddings, target_data)\ntrain_logits, infer_logits = decoding_layer(dec_embed_input, dec_embeddings, encoder_state,\\\ntarget_vocab_size, sequence_length, rnn_size, num_layers,\\\ntarget_vocab_to_int, keep_prob)\nreturn train_logits, infer_logits\n"""\nDON\'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n"""\ntests.test_seq2seq_model(seq2seq_model)'}, {'role': 'assistant', 'content': "['modelling']"}]