[{'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:# Parametric architecture\n\ndef tabular_dnn(numeric_variables, categorical_variables, categorical_counts,\n                feature_selection_dropout=0.2, categorical_dropout=0.1,\n                first_dense = 256, second_dense = 256, dense_dropout = 0.2, \n                activation_type=gelu):\n    \n    numerical_inputs = Input(shape=(len(numeric_variables),))\n    numerical_normalization = BatchNormalization()(numerical_inputs)\n    numerical_feature_selection = Dropout(feature_selection_dropout)(numerical_normalization)\n\n    categorical_inputs = []\n    categorical_embeddings = []\n    for category in  categorical_variables:\n        categorical_inputs.append(Input(shape=[1], name=category))\n        category_counts = categorical_counts[category]\n        categorical_embeddings.append(\n            Embedding(category_counts+1, \n                      int(np.log1p(category_counts)+1), \n                      name = category + "_embed")(categorical_inputs[-1]))\n\n    categorical_logits = Concatenate(name = "categorical_conc")([Flatten()(SpatialDropout1D(categorical_dropout)(cat_emb)) \n                                                                 for cat_emb in categorical_embeddings])\n\n    x = concatenate([numerical_feature_selection, categorical_logits])\n    x = Dense(first_dense, activation=activation_type)(x)\n    x = Dropout(dense_dropout)(x)  \n    x = Dense(second_dense, activation=activation_type)(x)\n    x = Dropout(dense_dropout)(x)\n    output = Dense(1, activation="sigmoid")(x)\n    model = Model([numerical_inputs] + categorical_inputs, output)\n    \n    return model'}, {'role': 'assistant', 'content': "['modelling']"}]