[{'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 decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size,\nnum_layers, target_vocab_to_int, keep_prob):\n"""\nCreate decoding layer\n:param dec_embed_input: Decoder embedded input\n:param dec_embeddings: Decoder embeddings\n:param encoder_state: The encoded state\n:param vocab_size: Size of vocabulary\n:param sequence_length: Sequence Length\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:param keep_prob: Dropout keep probability\n:return: Tuple of (Training Logits, Inference Logits)\n"""\nbasic_cell = tf.contrib.rnn.BasicLSTMCell(rnn_size)\nbasic_cell_dropout = tf.contrib.rnn.DropoutWrapper(basic_cell, output_keep_prob=keep_prob)\ndec_cell = tf.contrib.rnn.MultiRNNCell([basic_cell_dropout] * num_layers)\nstart_of_sequence_id = target_vocab_to_int[\'<GO>\']\nend_of_sequence_id = target_vocab_to_int[\'<EOS>\']\nmaximum_length = sequence_length - 1\nwith tf.variable_scope("decoding") as decoding_scope:\noutput_fn = lambda x: tf.contrib.layers.fully_connected(x, vocab_size, None, scope=decoding_scope)\ntrain_logits = decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, \\\ndecoding_scope, output_fn, keep_prob)\ndecoding_scope.reuse_variables()\ninference_logits = decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id,\\\nend_of_sequence_id, maximum_length, vocab_size, decoding_scope,\\\noutput_fn, keep_prob)\nreturn train_logits, inference_logits\n"""\nDON\'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n"""\ntests.test_decoding_layer(decoding_layer)'}, {'role': 'assistant', 'content': "['modelling']"}]