#pragma once
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#include <torch/csrc/jit/ir.h>
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#include <torch/csrc/utils/object_ptr.h>
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namespace torch {
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namespace jit {
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void initPythonIRBindings(PyObject* module);
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// execute a Python function, used for Ops we can't optimize but that we want to
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// optimize around
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struct ConcretePythonOp : public PythonOp {
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static Symbol Kind;
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ConcretePythonOp(Graph* graph) : PythonOp(graph, ::c10::prim::PythonOp) {}
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ConcretePythonOp* init(
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THPObjectPtr&& pyobj,
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const std::string& cconv,
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pyobj_list&& scalar_args) {
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this->pyobj = std::move(pyobj);
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this->scalar_args = std::move(scalar_args);
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this->cconv = cconv;
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return this;
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}
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// The Python object which contains the implementation of this function.
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// This is either a class (non-legacy) or an object (legacy). See
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// TraceInterpreterState for execution semantics.
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THPObjectPtr pyobj;
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// The calling convention for the Python function.
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// 'c' -- constant argument
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// 'd' -- dynamic argument
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std::string cconv;
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// Scalar arguments to the Python function. Not necessarily passed to
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// the function in this order; see cconv for the correct order.
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std::vector<THPObjectPtr> scalar_args;
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std::string name() const override;
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void cloneFrom(Node* other_) override;
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Node* allocNewInstance(Graph* g) override {
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return new ConcretePythonOp(g);
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}
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// recover the autograd.Function instance, if this PythonOp's function
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// was originally SomeFunction.apply
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// used in ONNX for discovering symbolics
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c10::optional<THPObjectPtr> autogradFunction() const override;
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void writeScalars(std::ostream& out) const override;
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void lint_python() const override;
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};
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}
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} // namespace torch
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