#pragma once // @generated by torchgen/gen.py from Function.h #include #include #include #include #include #include #include #include #include #include #include #include #include namespace at { // aten::batch_norm_backward_elemt(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor mean_dy, Tensor mean_dy_xmu, Tensor count) -> Tensor inline at::Tensor batch_norm_backward_elemt(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & weight, const at::Tensor & mean_dy, const at::Tensor & mean_dy_xmu, const at::Tensor & count) { return at::_ops::batch_norm_backward_elemt::call(grad_out, input, mean, invstd, weight, mean_dy, mean_dy_xmu, count); } // aten::batch_norm_backward_elemt.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor mean_dy, Tensor mean_dy_xmu, Tensor count, *, Tensor(a!) out) -> Tensor(a!) inline at::Tensor & batch_norm_backward_elemt_out(at::Tensor & out, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & weight, const at::Tensor & mean_dy, const at::Tensor & mean_dy_xmu, const at::Tensor & count) { return at::_ops::batch_norm_backward_elemt_out::call(grad_out, input, mean, invstd, weight, mean_dy, mean_dy_xmu, count, out); } // aten::batch_norm_backward_elemt.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor mean_dy, Tensor mean_dy_xmu, Tensor count, *, Tensor(a!) out) -> Tensor(a!) inline at::Tensor & batch_norm_backward_elemt_outf(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & weight, const at::Tensor & mean_dy, const at::Tensor & mean_dy_xmu, const at::Tensor & count, at::Tensor & out) { return at::_ops::batch_norm_backward_elemt_out::call(grad_out, input, mean, invstd, weight, mean_dy, mean_dy_xmu, count, out); } }