basic-add-logic.ipynb

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    "## Add Logic\n",
    "\n",
    "To add logic to the Flow, use the `uses` parameter to attach a Pod with an [Executor](https://github.com/jina-ai/jina/tree/master/docs/chapters/101#executors). `uses` accepts multiple value types including class name, Docker image, (inline) YAML or built-in shortcut.\n",
    "\n",
    "Let's first build a dummy encoder"
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    "from jina import Encoder\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class DummyEncoder(Encoder):\n",
    "    \n",
    "    def __init__(self, param=10, *args, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self.param = param\n",
    "        \n",
    "    def encode(self, data, *args, **kwargs):\n",
    "        return np.random.random([2, self.param])"
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    "To use `DummyEncoder` in the Flow, you can use the class name, a YAML file/literal, or a Python `dict`:"
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    "from jina import Flow\n",
    "f = (Flow().add(uses='DummyEncoder')\n",
    "       .add(uses={'__cls': 'DummyEncoder', 'with': {'param': 12}})  # dict config object with __cls keyword\n",
    "       .add(uses='myencoder.yml')  # YAML serialization of a Jina Executor\n",
    "       .add(uses='!DummyEncoder | {param: 20}'))  # inline YAML config\n",
    "       "
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    "The power of Jina lies in its decentralized architecture: each `add` creates a new Pod, and these Pods can be run as a local thread/process, a remote process, inside a Docker container, or even inside a remote Docker container.\n",
    "\n",
    "You can also use Docker image with `docker://` as prefix:"
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   "source": [
    "f.add(uses='docker://jinahub/pretrained-cnn:latest')\n"
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