prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  model/text_classification 使用内置语料集训练模型后，预测时出错，具体如下
  ```text
  I1123 09:23:54.320137    84 Util.cpp:166] commandline:  --use_gpu=False --trainer_count=1 
  Traceback (most recent call last):
    File "infer.py", line 91, in <module>
      batch_size=10)
    File "infer.py", line 58, in infer
      for idx, item in enumerate(test_reader):
  TypeError: 'function' object is not iterable
  ```
  paddle镜像用的是https://hub.docker.com/r/paddlepaddle/paddle/tags/ latest的，model下载的最新的
  
  </issue>
  <code>
  [start of text_classification/infer.py]
  1 import sys
  2 import os
  3 import gzip
  4 
  5 import paddle.v2 as paddle
  6 
  7 import reader
  8 from network_conf import fc_net, convolution_net
  9 from utils import logger, load_dict, load_reverse_dict
  10 
  11 
  12 def infer(topology, data_dir, model_path, word_dict_path, label_dict_path,
  13           batch_size):
  14     def _infer_a_batch(inferer, test_batch, ids_2_word, ids_2_label):
  15         probs = inferer.infer(input=test_batch, field=["value"])
  16         assert len(probs) == len(test_batch)
  17         for word_ids, prob in zip(test_batch, probs):
  18             word_text = " ".join([ids_2_word[id] for id in word_ids[0]])
  19             print("%s\t%s\t%s" % (ids_2_label[prob.argmax()],
  20                                   " ".join(["{:0.4f}".format(p)
  21                                             for p in prob]), word_text))
  22 
  23     logger.info("begin to predict...")
  24     use_default_data = (data_dir is None)
  25 
  26     if use_default_data:
  27         word_dict = paddle.dataset.imdb.word_dict()
  28         word_reverse_dict = dict((value, key)
  29                                  for key, value in word_dict.iteritems())
  30         label_reverse_dict = {0: "positive", 1: "negative"}
  31         test_reader = paddle.dataset.imdb.test(word_dict)
  32     else:
  33         assert os.path.exists(
  34             word_dict_path), "the word dictionary file does not exist"
  35         assert os.path.exists(
  36             label_dict_path), "the label dictionary file does not exist"
  37 
  38         word_dict = load_dict(word_dict_path)
  39         word_reverse_dict = load_reverse_dict(word_dict_path)
  40         label_reverse_dict = load_reverse_dict(label_dict_path)
  41 
  42         test_reader = reader.test_reader(data_dir, word_dict)()
  43 
  44     dict_dim = len(word_dict)
  45     class_num = len(label_reverse_dict)
  46     prob_layer = topology(dict_dim, class_num, is_infer=True)
  47 
  48     # initialize PaddlePaddle
  49     paddle.init(use_gpu=False, trainer_count=1)
  50 
  51     # load the trained models
  52     parameters = paddle.parameters.Parameters.from_tar(
  53         gzip.open(model_path, "r"))
  54     inferer = paddle.inference.Inference(
  55         output_layer=prob_layer, parameters=parameters)
  56 
  57     test_batch = []
  58     for idx, item in enumerate(test_reader):
  59         test_batch.append([item[0]])
  60         if len(test_batch) == batch_size:
  61             _infer_a_batch(inferer, test_batch, word_reverse_dict,
  62                            label_reverse_dict)
  63             test_batch = []
  64 
  65     if len(test_batch):
  66         _infer_a_batch(inferer, test_batch, word_reverse_dict,
  67                        label_reverse_dict)
  68         test_batch = []
  69 
  70 
  71 if __name__ == "__main__":
  72     model_path = "models/dnn_params_pass_00000.tar.gz"
  73     assert os.path.exists(model_path), "the trained model does not exist."
  74 
  75     nn_type = "dnn"
  76     test_dir = None
  77     word_dict = None
  78     label_dict = None
  79 
  80     if nn_type == "dnn":
  81         topology = fc_net
  82     elif nn_type == "cnn":
  83         topology = convolution_net
  84 
  85     infer(
  86         topology=topology,
  87         data_dir=test_dir,
  88         word_dict_path=word_dict,
  89         label_dict_path=label_dict,
  90         model_path=model_path,
  91         batch_size=10)
  92
  [end of text_classification/infer.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/text_classification/infer.py b/text_classification/infer.py
  --- a/text_classification/infer.py
  +++ b/text_classification/infer.py
  @@ -28,7 +28,7 @@
           word_reverse_dict = dict((value, key)
                                    for key, value in word_dict.iteritems())
           label_reverse_dict = {0: "positive", 1: "negative"}
  -        test_reader = paddle.dataset.imdb.test(word_dict)
  +        test_reader = paddle.dataset.imdb.test(word_dict)()
       else:
           assert os.path.exists(
               word_dict_path), "the word dictionary file does not exist"
  
verification_info: {"golden_diff": "diff --git a/text_classification/infer.py b/text_classification/infer.py\n--- a/text_classification/infer.py\n+++ b/text_classification/infer.py\n@@ -28,7 +28,7 @@\n         word_reverse_dict = dict((value, key)\n                                  for key, value in word_dict.iteritems())\n         label_reverse_dict = {0: \"positive\", 1: \"negative\"}\n-        test_reader = paddle.dataset.imdb.test(word_dict)\n+        test_reader = paddle.dataset.imdb.test(word_dict)()\n     else:\n         assert os.path.exists(\n             word_dict_path), \"the word dictionary file does not exist\"\n", "issue": "model/text_classification \u4f7f\u7528\u5185\u7f6e\u8bed\u6599\u96c6\u8bad\u7ec3\u6a21\u578b\u540e\uff0c\u9884\u6d4b\u65f6\u51fa\u9519\uff0c\u5177\u4f53\u5982\u4e0b\n```text\r\nI1123 09:23:54.320137    84 Util.cpp:166] commandline:  --use_gpu=False --trainer_count=1 \r\nTraceback (most recent call last):\r\n  File \"infer.py\", line 91, in <module>\r\n    batch_size=10)\r\n  File \"infer.py\", line 58, in infer\r\n    for idx, item in enumerate(test_reader):\r\nTypeError: 'function' object is not iterable\r\n```\r\npaddle\u955c\u50cf\u7528\u7684\u662fhttps://hub.docker.com/r/paddlepaddle/paddle/tags/ latest\u7684\uff0cmodel\u4e0b\u8f7d\u7684\u6700\u65b0\u7684\n", "before_files": [{"content": "import sys\nimport os\nimport gzip\n\nimport paddle.v2 as paddle\n\nimport reader\nfrom network_conf import fc_net, convolution_net\nfrom utils import logger, load_dict, load_reverse_dict\n\n\ndef infer(topology, data_dir, model_path, word_dict_path, label_dict_path,\n          batch_size):\n    def _infer_a_batch(inferer, test_batch, ids_2_word, ids_2_label):\n        probs = inferer.infer(input=test_batch, field=[\"value\"])\n        assert len(probs) == len(test_batch)\n        for word_ids, prob in zip(test_batch, probs):\n            word_text = \" \".join([ids_2_word[id] for id in word_ids[0]])\n            print(\"%s\\t%s\\t%s\" % (ids_2_label[prob.argmax()],\n                                  \" \".join([\"{:0.4f}\".format(p)\n                                            for p in prob]), word_text))\n\n    logger.info(\"begin to predict...\")\n    use_default_data = (data_dir is None)\n\n    if use_default_data:\n        word_dict = paddle.dataset.imdb.word_dict()\n        word_reverse_dict = dict((value, key)\n                                 for key, value in word_dict.iteritems())\n        label_reverse_dict = {0: \"positive\", 1: \"negative\"}\n        test_reader = paddle.dataset.imdb.test(word_dict)\n    else:\n        assert os.path.exists(\n            word_dict_path), \"the word dictionary file does not exist\"\n        assert os.path.exists(\n            label_dict_path), \"the label dictionary file does not exist\"\n\n        word_dict = load_dict(word_dict_path)\n        word_reverse_dict = load_reverse_dict(word_dict_path)\n        label_reverse_dict = load_reverse_dict(label_dict_path)\n\n        test_reader = reader.test_reader(data_dir, word_dict)()\n\n    dict_dim = len(word_dict)\n    class_num = len(label_reverse_dict)\n    prob_layer = topology(dict_dim, class_num, is_infer=True)\n\n    # initialize PaddlePaddle\n    paddle.init(use_gpu=False, trainer_count=1)\n\n    # load the trained models\n    parameters = paddle.parameters.Parameters.from_tar(\n        gzip.open(model_path, \"r\"))\n    inferer = paddle.inference.Inference(\n        output_layer=prob_layer, parameters=parameters)\n\n    test_batch = []\n    for idx, item in enumerate(test_reader):\n        test_batch.append([item[0]])\n        if len(test_batch) == batch_size:\n            _infer_a_batch(inferer, test_batch, word_reverse_dict,\n                           label_reverse_dict)\n            test_batch = []\n\n    if len(test_batch):\n        _infer_a_batch(inferer, test_batch, word_reverse_dict,\n                       label_reverse_dict)\n        test_batch = []\n\n\nif __name__ == \"__main__\":\n    model_path = \"models/dnn_params_pass_00000.tar.gz\"\n    assert os.path.exists(model_path), \"the trained model does not exist.\"\n\n    nn_type = \"dnn\"\n    test_dir = None\n    word_dict = None\n    label_dict = None\n\n    if nn_type == \"dnn\":\n        topology = fc_net\n    elif nn_type == \"cnn\":\n        topology = convolution_net\n\n    infer(\n        topology=topology,\n        data_dir=test_dir,\n        word_dict_path=word_dict,\n        label_dict_path=label_dict,\n        model_path=model_path,\n        batch_size=10)\n", "path": "text_classification/infer.py"}]}