[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:def build_mlp_model(input_shape, num_output):\n\\tX_input = layers.Input(input_shape)\n\n\\t# First dense layer\n\\tX = layers.Dense(dense_layer_sizes[0], name = \'dense0\')(X_input)\n\\tX = layers.BatchNormalization(name = \'bn0\')(X)\n\\tX = layers.Activation(\'relu\')(X)\n\\tX = layers.Dropout(dropout_rate, seed = 7)(X)\n#\\tX = IsLayer()(X)\n\n\\t# Second dense layer\n# \\tX = layers.Dense(dense_layer_sizes[0], name = \'dense1\')(X)\n# \\tX = layers.BatchNormalization(name = \'bn1\')(X)\n# \\tX = layers.Activation(\'relu\')(X)\n# \\tX = layers.Dropout(dropout_rate, seed = 9)(X)\n\n\\t# Output layer\n\\tX = layers.Dense(num_output, name = \'output\', kernel_regularizer = regularizers.l2(lambd))(X)\n\\tX = layers.Activation(\'sigmoid\')(X)\n\n\\t# Create model\n\\tmodel = models.Model(input = X_input, output = X, name = "classif_model")\n\\treturn model\''}, {'role': 'assistant', 'content': 'modelling'}]