32 Tensor do_trapezoid(const Tensor& y, const Tensor& dx, int64_t dim) {  in do_trapezoid()  argument
33     Tensor left = y.slice(dim, 0, -1);  in do_trapezoid()
34     Tensor right = y.slice(dim, 1);  in do_trapezoid()
37     return ((left + right) * dx).sum(dim) / 2.;  in do_trapezoid()
42 Tensor do_trapezoid(const Tensor& y, double dx, int64_t dim) {  in do_trapezoid()  argument
43     return (y.sum(dim) - (y.select(dim, 0) + y.select(dim, -1)) * (0.5)) * dx;  in do_trapezoid()
46 Tensor zeros_like_except(const Tensor& y, int64_t dim) {  in zeros_like_except()  argument
48     dim = maybe_wrap_dim(dim, y.dim());  in zeros_like_except()
49     sizes.erase(sizes.begin() + dim);  in zeros_like_except()
53 Tensor do_cumulative_trapezoid(const Tensor& y, const Tensor& dx, int64_t dim) {  in do_cumulative_trapezoid()  argument
54     Tensor left = y.slice(dim, 0, -1);  in do_cumulative_trapezoid()
55     Tensor right = y.slice(dim, 1);  in do_cumulative_trapezoid()
57     return ((left + right) * dx).cumsum(dim) / 2.;  in do_cumulative_trapezoid()
60 Tensor do_cumulative_trapezoid(const Tensor& y, double dx, int64_t dim) {  in do_cumulative_trapezoid()  argument
61     Tensor left = y.slice(dim, 0, -1);  in do_cumulative_trapezoid()
62     Tensor right = y.slice(dim, 1);  in do_cumulative_trapezoid()
64     return (dx /2. * (left + right)).cumsum(dim);  in do_cumulative_trapezoid()
85 Tensor trapezoid(const Tensor& y, const Tensor& x, int64_t dim) {  in trapezoid()  argument
86     dim = maybe_wrap_dim(dim, y);  in trapezoid()
89     if (y.sym_size(dim) == 0) {  in trapezoid()
90         return zeros_like_except(y, dim);  in trapezoid()
96     if (x.dim() == 1) {  in trapezoid()
98         // dimension (1,1,...,n,...,1,1) based on dim and y.dim() so that, later on, 'dx'  in trapezoid()
100 …TORCH_CHECK(x.sym_size(0) == y.sym_size(dim), "trapezoid: There must be one `x` value for each sam…  in trapezoid()
101         SymDimVector new_sizes(y.dim(), 1); // shape = [1] * y.  in trapezoid()
102         new_sizes[dim] = x.sym_size(0); // shape[axis] = d.shape[0]  in trapezoid()
104     } else if (x.dim() < y.dim()) {  in trapezoid()
107 …// This allows the subsequent slicing operations to proceed with any 'dim' without going out of bo…  in trapezoid()
108         SymDimVector new_sizes = add_padding_to_shape(x.sym_sizes(), y.dim());  in trapezoid()
113     // Note the .slice operation reduces the dimension along 'dim' by 1,  in trapezoid()
115     Tensor x_left = x_viewed.slice(dim, 0, -1);  in trapezoid()
116     Tensor x_right = x_viewed.slice(dim, 1);  in trapezoid()
119     return do_trapezoid(y, dx, dim);  in trapezoid()
122 Tensor trapezoid(const Tensor& y, const Scalar& dx, int64_t dim) {  in trapezoid()  argument
124     if (y.sym_size(dim) == 0) {  in trapezoid()
125         return zeros_like_except(y, dim);  in trapezoid()
129     return do_trapezoid(y, dx.toDouble(), dim);  in trapezoid()
132 Tensor trapz(const Tensor& y, const Tensor& x, int64_t dim) {  in trapz()  argument
133     return at::native::trapezoid(y, x, dim);  in trapz()
136 Tensor trapz(const Tensor& y, double dx, int64_t dim) {  in trapz()  argument
137     return at::native::trapezoid(y, dx, dim);  in trapz()
140 Tensor cumulative_trapezoid(const Tensor& y, const Tensor& x, int64_t dim) {  in cumulative_trapezoid()  argument
141     dim = maybe_wrap_dim(dim, y);  in cumulative_trapezoid()
144     if (x.dim() == 1) {  in cumulative_trapezoid()
146 …TORCH_CHECK(x.sym_size(0) == y.sym_size(dim), "cumulative_trapezoid: There must be one `x` value f…  in cumulative_trapezoid()
147         SymDimVector new_sizes(y.dim(), 1); // shape = [1] * y.  in cumulative_trapezoid()
148         new_sizes[dim] = x.sym_size(0); // shape[axis] = d.shape[0]  in cumulative_trapezoid()
150     } else if (x.dim() < y.dim()) {  in cumulative_trapezoid()
152         SymDimVector new_sizes = add_padding_to_shape(x.sym_sizes(), y.dim());  in cumulative_trapezoid()
157     Tensor x_left = x_viewed.slice(dim, 0, -1);  in cumulative_trapezoid()
158     Tensor x_right = x_viewed.slice(dim, 1);  in cumulative_trapezoid()
161     return do_cumulative_trapezoid(y, dx, dim);  in cumulative_trapezoid()
164 Tensor cumulative_trapezoid(const Tensor& y, const Scalar& dx, int64_t dim) {  in cumulative_trapezoid()  argument
168     return do_cumulative_trapezoid(y, dx.toDouble(), dim);  in cumulative_trapezoid()