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- from leads import L
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+ from abc import ABCMeta as _ABCMeta , abstractmethod as _abstractmethod
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+
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+ from leads .logger import L
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+ from .._computational import array as _array
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+
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+
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+ class JarvisBackend (object , metaclass = _ABCMeta ):
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+ @_abstractmethod
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+ def predict (self , x : _array ) -> tuple [float , float ]:
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+ raise NotImplementedError
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class Jarvis (object ):
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- def __init__ (self ) -> None :
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+ def __init__ (self , backend : JarvisBackend ) -> None :
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+ self .backend : JarvisBackend = backend
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L .info ("Jarvis is enabled\n "
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"Before proceeding, you must read the following Terms and Conditions carefully:\n "
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"Jarvis is a deep learning model that aims to provide the optimal operation based on limited inputs.\n "
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