Source code for onnx_diagnostic.reference.torch_ops.unary_ops

import torch
from . import OpRunKernel, OpRunTensor



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class Abs_1(OpRunKernel):
    """Abs"""


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    def run(self, x: OpRunTensor) -> OpRunTensor:
        return OpRunTensor(torch.abs(x.tensor))





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class Cos_1(OpRunKernel):
    """Cos"""


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    def run(self, x: OpRunTensor) -> OpRunTensor:
        return OpRunTensor(x.tensor.cos())





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class Erf_9(OpRunKernel):
    """Erf"""


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    def run(self, x: OpRunTensor) -> OpRunTensor:
        return OpRunTensor(x.tensor.erf())





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class Exp_1(OpRunKernel):
    """Exp"""


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    def run(self, x: OpRunTensor) -> OpRunTensor:
        return OpRunTensor(x.tensor.exp())





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class Identity_1(OpRunKernel):
    "Identity"


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    def run(self, x: OpRunTensor) -> OpRunTensor:
        return OpRunTensor(x.tensor)





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class Log_1(OpRunKernel):
    """Log"""


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    def run(self, x: OpRunTensor) -> OpRunTensor:
        return OpRunTensor(x.tensor.log())





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class Neg_1(OpRunKernel):
    """Neg"""


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    def run(self, x: OpRunTensor) -> OpRunTensor:
        return OpRunTensor(-x.tensor)





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class Not_1(OpRunKernel):
    """Not"""


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    def run(self, x: OpRunTensor) -> OpRunTensor:
        return OpRunTensor(~x.tensor)





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class Reciprocal_1(OpRunKernel):
    """REciprocal"""


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    def run(self, x: OpRunTensor) -> OpRunTensor:
        return OpRunTensor(1 / x.tensor)





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class Sigmoid_6(OpRunKernel):
    """Sqrt"""


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    def run(self, x: OpRunTensor) -> OpRunTensor:
        return OpRunTensor(torch.sigmoid(x.tensor))





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class Sin_1(OpRunKernel):
    """Sin"""


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    def run(self, x: OpRunTensor) -> OpRunTensor:
        return OpRunTensor(x.tensor.sin())





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class Sqrt_1(OpRunKernel):
    """Sqrt"""


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    def run(self, x: OpRunTensor) -> OpRunTensor:
        return OpRunTensor(x.tensor.sqrt())