[{'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:"""\nmnist_loader\n~~~~~~~~~~~~\nA library to load the MNIST image data.  For details of the data\nstructures that are returned, see the doc strings for ``load_data``\nand ``load_data_wrapper``.  In practice, ``load_data_wrapper`` is the\nfunction usually called by our neural network code.\n"""\nimport pickle as cPickle  # Sorry, workaround for Python 3.4\nimport gzip\nimport numpy as np\ndef load_data():\nf = gzip.open(\'mnist.pkl.gz\', \'rb\')\ntraining_data, validation_data, test_data = cPickle.load(f, encoding=\'latin1\')  # Make it work with Py3.x\nf.close()\nreturn (training_data, validation_data, test_data)\ndef load_data_wrapper():\ntr_d, va_d, te_d = load_data()\ntraining_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]]\ntraining_results = [vectorized_result(y) for y in tr_d[1]]\ntraining_data = zip(training_inputs, training_results)\nvalidation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]]\nvalidation_data = zip(validation_inputs, va_d[1])\ntest_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]]\ntest_data = zip(test_inputs, te_d[1])\nreturn (training_data, validation_data, test_data)\ndef vectorized_result(j):\ne = np.zeros((10, 1))\ne[j] = 1.0\nreturn e'}, {'role': 'assistant', 'content': "['helper_functions', 'load_data']"}]