We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Exception in multi label classification with StructuredDataClassifier
### Bug Description
Multi label classification throws the following error when using `StructuredDataClassifier`:

```
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-23-e7c45e9f8b2f> in <module>
----> 1 clf.predict(X)

/opt/conda/lib/python3.7/site-packages/autokeras/tasks/structured_data.py in predict(self, x, batch_size, **kwargs)
    156         x = self.read_for_predict(x)
    157 
--> 158         return super().predict(x=x, batch_size=batch_size, **kwargs)
    159 
    160     def evaluate(self, x, y=None, batch_size=32, **kwargs):

/opt/conda/lib/python3.7/site-packages/autokeras/auto_model.py in predict(self, x, **kwargs)
    421         self._check_data_format((x, None), predict=True)
    422         dataset = self._adapt(x, self.inputs)
--> 423         pipeline = self.tuner.get_best_pipeline()
    424         model = self.tuner.get_best_model()
    425         dataset = pipeline.transform_x(dataset)

/opt/conda/lib/python3.7/site-packages/autokeras/engine/tuner.py in get_best_pipeline(self)
     65 
     66     def get_best_pipeline(self):
---> 67         return pipeline_module.load_pipeline(self.best_pipeline_path)
     68 
     69     def _pipeline_path(self, trial_id):

/opt/conda/lib/python3.7/site-packages/autokeras/pipeline.py in load_pipeline(filepath, custom_objects)
     73         custom_objects = {}
     74     with tf.keras.utils.custom_object_scope(custom_objects):
---> 75         return Pipeline.from_config(utils.load_json(filepath))
     76 
     77 

/opt/conda/lib/python3.7/site-packages/autokeras/pipeline.py in from_config(cls, config)
    180                     for preprocessor in preprocessors
    181                 ]
--> 182                 for preprocessors in config["outputs"]
    183             ],
    184         )

/opt/conda/lib/python3.7/site-packages/autokeras/pipeline.py in <listcomp>(.0)
    180                     for preprocessor in preprocessors
    181                 ]
--> 182                 for preprocessors in config["outputs"]
    183             ],
    184         )

/opt/conda/lib/python3.7/site-packages/autokeras/pipeline.py in <listcomp>(.0)
    178                 [
    179                     preprocessors_module.deserialize(preprocessor)
--> 180                     for preprocessor in preprocessors
    181                 ]
    182                 for preprocessors in config["outputs"]

/opt/conda/lib/python3.7/site-packages/autokeras/preprocessors/__init__.py in deserialize(config, custom_objects)
     33         module_objects=globals(),
     34         custom_objects=custom_objects,
---> 35         printable_module_name="preprocessors",
     36     )

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    358                 list(custom_objects.items())))
    359       with CustomObjectScope(custom_objects):
--> 360         return cls.from_config(cls_config)
    361     else:
    362       # Then `cls` may be a function returning a class.

/opt/conda/lib/python3.7/site-packages/autokeras/engine/serializable.py in from_config(cls, config)
     32             config: Dict. The config of the object.
     33         """
---> 34         return cls(**config)

/opt/conda/lib/python3.7/site-packages/autokeras/preprocessors/encoders.py in __init__(self, **kwargs)
    125 
    126     def __init__(self, **kwargs):
--> 127         super().__init__(labels=[], **kwargs)
    128 
    129     def transform(self, dataset):

TypeError: __init__() got multiple values for keyword argument 'labels'
```

### Bug Reproduction
Code for reproducing the bug:

```
from sklearn.datasets import make_multilabel_classification
import autokeras as ak

X, y = make_multilabel_classification(n_samples=1000, n_features=20, n_classes=2, n_labels=5)
clf = ak.StructuredDataClassifier(
    overwrite=True,
    max_trials=3,
    multi_label=True,
    objective='val_loss',
    loss='binary_crossentropy'
)
clf.fit(X, y, epochs=3)
clf.predict(X)
```

### Expected Behavior
This should not throw an error, but return the predictions of the multi label classification

### Setup Details
Include the details about the versions of:
 - OS type and version: Ubuntu 18.04.4
 - Python: 3.7.6
 - autokeras: 1.0.9
 - keras-tuner: 1.0.2rc2
 - scikit-learn: 0.23.2
 - numpy: 1.18.5
 - pandas: 1.1.2
 - tensorflow: 2.3.0


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `autokeras/preprocessors/encoders.py`
Content:
```
1 # Copyright 2020 The AutoKeras Authors.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
6 #
7 #     http://www.apache.org/licenses/LICENSE-2.0
8 #
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 
15 import numpy as np
16 import tensorflow as tf
17 
18 from autokeras.engine import preprocessor
19 
20 
21 class Encoder(preprocessor.TargetPreprocessor):
22     """Transform labels to encodings.
23 
24     # Arguments
25         labels: A list of labels of any type. The labels to be encoded.
26     """
27 
28     def __init__(self, labels, **kwargs):
29         super().__init__(**kwargs)
30         self.labels = [
31             label.decode("utf-8") if isinstance(label, bytes) else str(label)
32             for label in labels
33         ]
34 
35     def get_config(self):
36         return {"labels": self.labels}
37 
38     def fit(self, dataset):
39         return
40 
41     def transform(self, dataset):
42         """Transform labels to integer encodings.
43 
44         # Arguments
45             dataset: tf.data.Dataset. The dataset to be transformed.
46 
47         # Returns
48             tf.data.Dataset. The transformed dataset.
49         """
50         keys_tensor = tf.constant(self.labels)
51         vals_tensor = tf.constant(list(range(len(self.labels))))
52         table = tf.lookup.StaticHashTable(
53             tf.lookup.KeyValueTensorInitializer(keys_tensor, vals_tensor), -1
54         )
55 
56         return dataset.map(lambda x: table.lookup(tf.reshape(x, [-1])))
57 
58 
59 class OneHotEncoder(Encoder):
60     def transform(self, dataset):
61         """Transform labels to one-hot encodings.
62 
63         # Arguments
64             dataset: tf.data.Dataset. The dataset to be transformed.
65 
66         # Returns
67             tf.data.Dataset. The transformed dataset.
68         """
69         dataset = super().transform(dataset)
70         eye = tf.eye(len(self.labels))
71         dataset = dataset.map(lambda x: tf.nn.embedding_lookup(eye, x))
72         return dataset
73 
74     def postprocess(self, data):
75         """Transform probabilities back to labels.
76 
77         # Arguments
78             data: numpy.ndarray. The output probabilities of the classification head.
79 
80         # Returns
81             numpy.ndarray. The original labels.
82         """
83         return np.array(
84             list(
85                 map(
86                     lambda x: self.labels[x],
87                     np.argmax(np.array(data), axis=1),
88                 )
89             )
90         ).reshape(-1, 1)
91 
92 
93 class LabelEncoder(Encoder):
94     """Transform the labels to integer encodings."""
95 
96     def transform(self, dataset):
97         """Transform labels to integer encodings.
98 
99         # Arguments
100             dataset: tf.data.Dataset. The dataset to be transformed.
101 
102         # Returns
103             tf.data.Dataset. The transformed dataset.
104         """
105         dataset = super().transform(dataset)
106         dataset = dataset.map(lambda x: tf.expand_dims(x, axis=-1))
107         return dataset
108 
109     def postprocess(self, data):
110         """Transform probabilities back to labels.
111 
112         # Arguments
113             data: numpy.ndarray. The output probabilities of the classification head.
114 
115         # Returns
116             numpy.ndarray. The original labels.
117         """
118         return np.array(
119             list(map(lambda x: self.labels[int(round(x[0]))], np.array(data)))
120         ).reshape(-1, 1)
121 
122 
123 class MultiLabelEncoder(Encoder):
124     """Encoder for multi-label data."""
125 
126     def __init__(self, **kwargs):
127         super().__init__(labels=[], **kwargs)
128 
129     def transform(self, dataset):
130         return dataset
131 
132     def postprocess(self, data):
133         """Transform probabilities to zeros and ones.
134 
135         # Arguments
136             data: numpy.ndarray. The output probabilities of the classification head.
137 
138         # Returns
139             numpy.ndarray. The zeros and ones predictions.
140         """
141         data[data < 0.5] = 0
142         data[data > 0.5] = 1
143         return data
144
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/autokeras/preprocessors/encoders.py b/autokeras/preprocessors/encoders.py
--- a/autokeras/preprocessors/encoders.py
+++ b/autokeras/preprocessors/encoders.py
@@ -124,6 +124,7 @@
     """Encoder for multi-label data."""
 
     def __init__(self, **kwargs):
+        kwargs.pop("labels", None)
         super().__init__(labels=[], **kwargs)
 
     def transform(self, dataset):

{"golden_diff": "diff --git a/autokeras/preprocessors/encoders.py b/autokeras/preprocessors/encoders.py\n--- a/autokeras/preprocessors/encoders.py\n+++ b/autokeras/preprocessors/encoders.py\n@@ -124,6 +124,7 @@\n     \"\"\"Encoder for multi-label data.\"\"\"\n \n     def __init__(self, **kwargs):\n+        kwargs.pop(\"labels\", None)\n         super().__init__(labels=[], **kwargs)\n \n     def transform(self, dataset):\n", "issue": "Exception in multi label classification with StructuredDataClassifier\n### Bug Description\r\nMulti label classification throws the following error when using `StructuredDataClassifier`:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-23-e7c45e9f8b2f> in <module>\r\n----> 1 clf.predict(X)\r\n\r\n/opt/conda/lib/python3.7/site-packages/autokeras/tasks/structured_data.py in predict(self, x, batch_size, **kwargs)\r\n    156         x = self.read_for_predict(x)\r\n    157 \r\n--> 158         return super().predict(x=x, batch_size=batch_size, **kwargs)\r\n    159 \r\n    160     def evaluate(self, x, y=None, batch_size=32, **kwargs):\r\n\r\n/opt/conda/lib/python3.7/site-packages/autokeras/auto_model.py in predict(self, x, **kwargs)\r\n    421         self._check_data_format((x, None), predict=True)\r\n    422         dataset = self._adapt(x, self.inputs)\r\n--> 423         pipeline = self.tuner.get_best_pipeline()\r\n    424         model = self.tuner.get_best_model()\r\n    425         dataset = pipeline.transform_x(dataset)\r\n\r\n/opt/conda/lib/python3.7/site-packages/autokeras/engine/tuner.py in get_best_pipeline(self)\r\n     65 \r\n     66     def get_best_pipeline(self):\r\n---> 67         return pipeline_module.load_pipeline(self.best_pipeline_path)\r\n     68 \r\n     69     def _pipeline_path(self, trial_id):\r\n\r\n/opt/conda/lib/python3.7/site-packages/autokeras/pipeline.py in load_pipeline(filepath, custom_objects)\r\n     73         custom_objects = {}\r\n     74     with tf.keras.utils.custom_object_scope(custom_objects):\r\n---> 75         return Pipeline.from_config(utils.load_json(filepath))\r\n     76 \r\n     77 \r\n\r\n/opt/conda/lib/python3.7/site-packages/autokeras/pipeline.py in from_config(cls, config)\r\n    180                     for preprocessor in preprocessors\r\n    181                 ]\r\n--> 182                 for preprocessors in config[\"outputs\"]\r\n    183             ],\r\n    184         )\r\n\r\n/opt/conda/lib/python3.7/site-packages/autokeras/pipeline.py in <listcomp>(.0)\r\n    180                     for preprocessor in preprocessors\r\n    181                 ]\r\n--> 182                 for preprocessors in config[\"outputs\"]\r\n    183             ],\r\n    184         )\r\n\r\n/opt/conda/lib/python3.7/site-packages/autokeras/pipeline.py in <listcomp>(.0)\r\n    178                 [\r\n    179                     preprocessors_module.deserialize(preprocessor)\r\n--> 180                     for preprocessor in preprocessors\r\n    181                 ]\r\n    182                 for preprocessors in config[\"outputs\"]\r\n\r\n/opt/conda/lib/python3.7/site-packages/autokeras/preprocessors/__init__.py in deserialize(config, custom_objects)\r\n     33         module_objects=globals(),\r\n     34         custom_objects=custom_objects,\r\n---> 35         printable_module_name=\"preprocessors\",\r\n     36     )\r\n\r\n/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)\r\n    358                 list(custom_objects.items())))\r\n    359       with CustomObjectScope(custom_objects):\r\n--> 360         return cls.from_config(cls_config)\r\n    361     else:\r\n    362       # Then `cls` may be a function returning a class.\r\n\r\n/opt/conda/lib/python3.7/site-packages/autokeras/engine/serializable.py in from_config(cls, config)\r\n     32             config: Dict. The config of the object.\r\n     33         \"\"\"\r\n---> 34         return cls(**config)\r\n\r\n/opt/conda/lib/python3.7/site-packages/autokeras/preprocessors/encoders.py in __init__(self, **kwargs)\r\n    125 \r\n    126     def __init__(self, **kwargs):\r\n--> 127         super().__init__(labels=[], **kwargs)\r\n    128 \r\n    129     def transform(self, dataset):\r\n\r\nTypeError: __init__() got multiple values for keyword argument 'labels'\r\n```\r\n\r\n### Bug Reproduction\r\nCode for reproducing the bug:\r\n\r\n```\r\nfrom sklearn.datasets import make_multilabel_classification\r\nimport autokeras as ak\r\n\r\nX, y = make_multilabel_classification(n_samples=1000, n_features=20, n_classes=2, n_labels=5)\r\nclf = ak.StructuredDataClassifier(\r\n    overwrite=True,\r\n    max_trials=3,\r\n    multi_label=True,\r\n    objective='val_loss',\r\n    loss='binary_crossentropy'\r\n)\r\nclf.fit(X, y, epochs=3)\r\nclf.predict(X)\r\n```\r\n\r\n### Expected Behavior\r\nThis should not throw an error, but return the predictions of the multi label classification\r\n\r\n### Setup Details\r\nInclude the details about the versions of:\r\n - OS type and version: Ubuntu 18.04.4\r\n - Python: 3.7.6\r\n - autokeras: 1.0.9\r\n - keras-tuner: 1.0.2rc2\r\n - scikit-learn: 0.23.2\r\n - numpy: 1.18.5\r\n - pandas: 1.1.2\r\n - tensorflow: 2.3.0\r\n\n", "before_files": [{"content": "# Copyright 2020 The AutoKeras Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom autokeras.engine import preprocessor\n\n\nclass Encoder(preprocessor.TargetPreprocessor):\n    \"\"\"Transform labels to encodings.\n\n    # Arguments\n        labels: A list of labels of any type. The labels to be encoded.\n    \"\"\"\n\n    def __init__(self, labels, **kwargs):\n        super().__init__(**kwargs)\n        self.labels = [\n            label.decode(\"utf-8\") if isinstance(label, bytes) else str(label)\n            for label in labels\n        ]\n\n    def get_config(self):\n        return {\"labels\": self.labels}\n\n    def fit(self, dataset):\n        return\n\n    def transform(self, dataset):\n        \"\"\"Transform labels to integer encodings.\n\n        # Arguments\n            dataset: tf.data.Dataset. The dataset to be transformed.\n\n        # Returns\n            tf.data.Dataset. The transformed dataset.\n        \"\"\"\n        keys_tensor = tf.constant(self.labels)\n        vals_tensor = tf.constant(list(range(len(self.labels))))\n        table = tf.lookup.StaticHashTable(\n            tf.lookup.KeyValueTensorInitializer(keys_tensor, vals_tensor), -1\n        )\n\n        return dataset.map(lambda x: table.lookup(tf.reshape(x, [-1])))\n\n\nclass OneHotEncoder(Encoder):\n    def transform(self, dataset):\n        \"\"\"Transform labels to one-hot encodings.\n\n        # Arguments\n            dataset: tf.data.Dataset. The dataset to be transformed.\n\n        # Returns\n            tf.data.Dataset. The transformed dataset.\n        \"\"\"\n        dataset = super().transform(dataset)\n        eye = tf.eye(len(self.labels))\n        dataset = dataset.map(lambda x: tf.nn.embedding_lookup(eye, x))\n        return dataset\n\n    def postprocess(self, data):\n        \"\"\"Transform probabilities back to labels.\n\n        # Arguments\n            data: numpy.ndarray. The output probabilities of the classification head.\n\n        # Returns\n            numpy.ndarray. The original labels.\n        \"\"\"\n        return np.array(\n            list(\n                map(\n                    lambda x: self.labels[x],\n                    np.argmax(np.array(data), axis=1),\n                )\n            )\n        ).reshape(-1, 1)\n\n\nclass LabelEncoder(Encoder):\n    \"\"\"Transform the labels to integer encodings.\"\"\"\n\n    def transform(self, dataset):\n        \"\"\"Transform labels to integer encodings.\n\n        # Arguments\n            dataset: tf.data.Dataset. The dataset to be transformed.\n\n        # Returns\n            tf.data.Dataset. The transformed dataset.\n        \"\"\"\n        dataset = super().transform(dataset)\n        dataset = dataset.map(lambda x: tf.expand_dims(x, axis=-1))\n        return dataset\n\n    def postprocess(self, data):\n        \"\"\"Transform probabilities back to labels.\n\n        # Arguments\n            data: numpy.ndarray. The output probabilities of the classification head.\n\n        # Returns\n            numpy.ndarray. The original labels.\n        \"\"\"\n        return np.array(\n            list(map(lambda x: self.labels[int(round(x[0]))], np.array(data)))\n        ).reshape(-1, 1)\n\n\nclass MultiLabelEncoder(Encoder):\n    \"\"\"Encoder for multi-label data.\"\"\"\n\n    def __init__(self, **kwargs):\n        super().__init__(labels=[], **kwargs)\n\n    def transform(self, dataset):\n        return dataset\n\n    def postprocess(self, data):\n        \"\"\"Transform probabilities to zeros and ones.\n\n        # Arguments\n            data: numpy.ndarray. The output probabilities of the classification head.\n\n        # Returns\n            numpy.ndarray. The zeros and ones predictions.\n        \"\"\"\n        data[data < 0.5] = 0\n        data[data > 0.5] = 1\n        return data\n", "path": "autokeras/preprocessors/encoders.py"}], "after_files": [{"content": "# Copyright 2020 The AutoKeras Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom autokeras.engine import preprocessor\n\n\nclass Encoder(preprocessor.TargetPreprocessor):\n    \"\"\"Transform labels to encodings.\n\n    # Arguments\n        labels: A list of labels of any type. The labels to be encoded.\n    \"\"\"\n\n    def __init__(self, labels, **kwargs):\n        super().__init__(**kwargs)\n        self.labels = [\n            label.decode(\"utf-8\") if isinstance(label, bytes) else str(label)\n            for label in labels\n        ]\n\n    def get_config(self):\n        return {\"labels\": self.labels}\n\n    def fit(self, dataset):\n        return\n\n    def transform(self, dataset):\n        \"\"\"Transform labels to integer encodings.\n\n        # Arguments\n            dataset: tf.data.Dataset. The dataset to be transformed.\n\n        # Returns\n            tf.data.Dataset. The transformed dataset.\n        \"\"\"\n        keys_tensor = tf.constant(self.labels)\n        vals_tensor = tf.constant(list(range(len(self.labels))))\n        table = tf.lookup.StaticHashTable(\n            tf.lookup.KeyValueTensorInitializer(keys_tensor, vals_tensor), -1\n        )\n\n        return dataset.map(lambda x: table.lookup(tf.reshape(x, [-1])))\n\n\nclass OneHotEncoder(Encoder):\n    def transform(self, dataset):\n        \"\"\"Transform labels to one-hot encodings.\n\n        # Arguments\n            dataset: tf.data.Dataset. The dataset to be transformed.\n\n        # Returns\n            tf.data.Dataset. The transformed dataset.\n        \"\"\"\n        dataset = super().transform(dataset)\n        eye = tf.eye(len(self.labels))\n        dataset = dataset.map(lambda x: tf.nn.embedding_lookup(eye, x))\n        return dataset\n\n    def postprocess(self, data):\n        \"\"\"Transform probabilities back to labels.\n\n        # Arguments\n            data: numpy.ndarray. The output probabilities of the classification head.\n\n        # Returns\n            numpy.ndarray. The original labels.\n        \"\"\"\n        return np.array(\n            list(\n                map(\n                    lambda x: self.labels[x],\n                    np.argmax(np.array(data), axis=1),\n                )\n            )\n        ).reshape(-1, 1)\n\n\nclass LabelEncoder(Encoder):\n    \"\"\"Transform the labels to integer encodings.\"\"\"\n\n    def transform(self, dataset):\n        \"\"\"Transform labels to integer encodings.\n\n        # Arguments\n            dataset: tf.data.Dataset. The dataset to be transformed.\n\n        # Returns\n            tf.data.Dataset. The transformed dataset.\n        \"\"\"\n        dataset = super().transform(dataset)\n        dataset = dataset.map(lambda x: tf.expand_dims(x, axis=-1))\n        return dataset\n\n    def postprocess(self, data):\n        \"\"\"Transform probabilities back to labels.\n\n        # Arguments\n            data: numpy.ndarray. The output probabilities of the classification head.\n\n        # Returns\n            numpy.ndarray. The original labels.\n        \"\"\"\n        return np.array(\n            list(map(lambda x: self.labels[int(round(x[0]))], np.array(data)))\n        ).reshape(-1, 1)\n\n\nclass MultiLabelEncoder(Encoder):\n    \"\"\"Encoder for multi-label data.\"\"\"\n\n    def __init__(self, **kwargs):\n        kwargs.pop(\"labels\", None)\n        super().__init__(labels=[], **kwargs)\n\n    def transform(self, dataset):\n        return dataset\n\n    def postprocess(self, data):\n        \"\"\"Transform probabilities to zeros and ones.\n\n        # Arguments\n            data: numpy.ndarray. The output probabilities of the classification head.\n\n        # Returns\n            numpy.ndarray. The zeros and ones predictions.\n        \"\"\"\n        data[data < 0.5] = 0\n        data[data > 0.5] = 1\n        return data\n", "path": "autokeras/preprocessors/encoders.py"}]}