Scalar native_functions Notes

Cases:

func: addmv(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor
func: addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
func: baddbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
func: s_native_addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
func: _sparse_addmm(Tensor self, Tensor sparse, Tensor dense, *, Scalar beta=1, Scalar alpha=1) -> Tensor
func: addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
func: addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
func: addmm_(Tensor(a!) self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
func: addbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
func: addcdiv_(Tensor(a!) self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor(a!)
func: sspaddmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
func: _addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
func: _addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
func: addcmul(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor
func: addcdiv(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor

func: arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)
func: linspace(Scalar start, Scalar end, int steps=100, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
func: logspace(Scalar start, Scalar end, int steps=100, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
func: scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
func: range.step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor

func: full(int[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
func: fill_.Scalar(Tensor(a!) self, Scalar value) -> Tensor(a!)
func: fill_diagonal_(Tensor(a!) self, Scalar fill_value, bool wrap=False) -> Tensor(a!)
func: masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -> Tensor
func: scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> Tensor
func: _pad_packed_sequence(Tensor data, Tensor batch_sizes, bool batch_first, Scalar padding_value, int total_length) -> (Tensor, Tensor)
func: constant_pad_nd(Tensor self, int[] pad, Scalar value=0) -> Tensor

func: add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
func: _sparse_add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
func: _sparse_dense_add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
func: _sparse_div_scalar.out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
func: _sparse_mul_scalar.out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
func: sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
func: sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
func: sub_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)
func: sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
func: sub_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
func: rsub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
func: rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
func: mul.Scalar(Tensor self, Scalar other) -> Tensor
func: div.Scalar(Tensor self, Scalar other) -> Tensor

func: clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor
func: rrelu(Tensor self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor
func: softplus(Tensor self, Scalar beta=1, Scalar threshold=20) -> Tensor
func: softshrink(Tensor self, Scalar lambd=0.5) -> Tensor
func: hardshrink(Tensor self, Scalar lambd=0.5) -> Tensor
func: celu(Tensor self, Scalar alpha=1.0) -> Tensor
func: threshold(Tensor self, Scalar threshold, Scalar value) -> Tensor
func: norm.Scalar(Tensor self, Scalar p=2) -> Tensor
func: renorm_(Tensor(a!) self, Scalar p, int dim, Scalar maxnorm) -> Tensor(a!)
func: multi_margin_loss(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean) -> Tensor
func: renorm(Tensor self, Scalar p, int dim, Scalar maxnorm) -> Tensor
func: dist(Tensor self, Tensor other, Scalar p=2) -> Tensor
func: histc(Tensor self, int bins=100, Scalar min=0, Scalar max=0) -> Tensor
func: elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor
func: hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor
func: leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor

Provide feedback