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Update examples
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docs/deployments/request-handlers.md

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```python
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import numpy as np
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labels = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
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labels = ["iris-setosa", "iris-versicolor", "iris-virginica"]
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def pre_inference(sample, metadata):
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# Convert a dictionary of features to a flattened in list in the order expected by the model

examples/iris/handlers/keras.py

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import numpy as np
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labels = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
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labels = ["iris-setosa", "iris-versicolor", "iris-virginica"]
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def pre_inference(sample, metadata):

examples/iris/handlers/pytorch.py

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import numpy as np
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labels = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
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labels = ["iris-setosa", "iris-versicolor", "iris-virginica"]
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def pre_inference(sample, metadata):

examples/iris/handlers/sklearn.py

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import numpy as np
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labels = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
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labels = ["iris-setosa", "iris-versicolor", "iris-virginica"]
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def pre_inference(sample, metadata):

examples/iris/handlers/tensorflow.py

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labels = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
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labels = ["iris-setosa", "iris-versicolor", "iris-virginica"]
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def post_inference(prediction, metadata):

examples/iris/handlers/xgboost.py

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import numpy as np
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labels = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
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labels = ["iris-setosa", "iris-versicolor", "iris-virginica"]
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def pre_inference(sample, metadata):

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