[{'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:class Model:\ndef __init__(self, batch_size, input_dims, phase = Phase.Predict):\nself._x = x = tf.placeholder(tf.float32, shape=[batch_size, input_dims])\nif phase != Phase.Predict:\nself._y = y = tf.placeholder(tf.int32, shape=[batch_size])\nw = tf.get_variable("w", shape = [input_dims, 1])\nb = tf.get_variable("b", shape = 1)\nlogits = tf.matmul(x, w) + b\nlogits = tf.reshape(logits, [-1])\nif phase == Phase.Train:\nlosses = tf.nn.sigmoid_cross_entropy_with_logits(labels = tf.cast(y, tf.float32), logits = logits)\nself._loss = loss = tf.reduce_sum(losses)\nself._train_op = tf.train.GradientDescentOptimizer(0.05).minimize(loss)\nelse:\nself._probs = probs = tf.sigmoid(logits)\nself._labels = tf.cast(tf.round(probs), tf.int32)\nif phase == Phase.Validation:\ncorrect = tf.equal(y, self.labels)\nself._accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\n@property\ndef x(self):\nreturn self._x\n@property\ndef y(self):\nreturn self._y\n@property\ndef labels(self):\nreturn self._labels\n@property\ndef loss(self):\nreturn self._loss\n@property\ndef train_op(self):\nreturn self._train_op\n@property\ndef probs(self):\nreturn self._probs\n@property\ndef accuracy(self):\nreturn self._accuracy'}, {'role': 'assistant', 'content': "['modelling']"}]