yobx.torch.interpreter._aten_methods#

yobx.torch.interpreter._aten_methods.aten_meth___eq__(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str, name='meth__eq___') str[source]#

equal

yobx.torch.interpreter._aten_methods.aten_meth_abs(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

abs.

yobx.torch.interpreter._aten_methods.aten_meth_bool(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

cast

yobx.torch.interpreter._aten_methods.aten_meth_clamp_max(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, max_: str, name: str = 'meth_clamp_max') str[source]#

meth_clamp_max

yobx.torch.interpreter._aten_methods.aten_meth_clamp_min(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, min_: str, name: str = 'meth_clamp_min') str[source]#

meth_clamp_min

yobx.torch.interpreter._aten_methods.aten_meth_clone(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

identity

yobx.torch.interpreter._aten_methods.aten_meth_contiguous(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

identity

yobx.torch.interpreter._aten_methods.aten_meth_cos(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

cos

yobx.torch.interpreter._aten_methods.aten_meth_cpu(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

identity

yobx.torch.interpreter._aten_methods.aten_meth_detach(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

identity

yobx.torch.interpreter._aten_methods.aten_meth_eq(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str, name='meth_eq') str[source]#

equal

yobx.torch.interpreter._aten_methods.aten_meth_exp(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

exp.

yobx.torch.interpreter._aten_methods.aten_meth_expand(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, *dims: List[int]) str[source]#

expand

yobx.torch.interpreter._aten_methods.aten_meth_expand_as(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, y: str, name: str = 'aten_meth_expand_as') str[source]#

expand_as

yobx.torch.interpreter._aten_methods.aten_meth_flatten(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, start_dim: int = 0, end_dim: int = -1) str[source]#

flatten.

yobx.torch.interpreter._aten_methods.aten_meth_float(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

cast

yobx.torch.interpreter._aten_methods.aten_meth_item(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, name: str = 'aten_meth_item') str[source]#

float(x)

yobx.torch.interpreter._aten_methods.aten_meth_log(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

log.

yobx.torch.interpreter._aten_methods.aten_meth_masked_fill(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, mask: str, value: Any, name: str = 'aten_meth_masked_fill') str[source]#

masked_fill

yobx.torch.interpreter._aten_methods.aten_meth_masked_fill_(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, mask: str, value: Any) str[source]#

masked

yobx.torch.interpreter._aten_methods.aten_meth_max(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: int | None = None, keepdim: bool = False) str[source]#

max.

yobx.torch.interpreter._aten_methods.aten_meth_mean(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: str | None = None, keepdim: bool = False) str[source]#

reducemean.

yobx.torch.interpreter._aten_methods.aten_meth_min(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: int | None = None, keepdim: bool = False) str[source]#

min.

yobx.torch.interpreter._aten_methods.aten_meth_neg(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

neg.

yobx.torch.interpreter._aten_methods.aten_meth_numel(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, name: str = 'meth_numel') str[source]#

meth_numel

yobx.torch.interpreter._aten_methods.aten_meth_permute(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, *dims: Sequence[int]) str[source]#

permute.

yobx.torch.interpreter._aten_methods.aten_meth_pow(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, exponent: str) str[source]#

pow

yobx.torch.interpreter._aten_methods.aten_meth_relu(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

relu.

yobx.torch.interpreter._aten_methods.aten_meth_repeat(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, *repeats: List[int]) str[source]#

repeat

yobx.torch.interpreter._aten_methods.aten_meth_reshape(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], input_name: str, *shape: List[int], name: str = 'reshape') str[source]#

reshape

yobx.torch.interpreter._aten_methods.aten_meth_shape(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, name: str = '.shape') str[source]#

shape

yobx.torch.interpreter._aten_methods.aten_meth_sigmoid(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

sigmoid.

yobx.torch.interpreter._aten_methods.aten_meth_sin(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

sin

yobx.torch.interpreter._aten_methods.aten_meth_size(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: int | None = None, name: str = '.size') str[source]#

size

yobx.torch.interpreter._aten_methods.aten_meth_softmax(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: int = -1) str[source]#

softmax.

yobx.torch.interpreter._aten_methods.aten_meth_sqrt(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

sqrt.

yobx.torch.interpreter._aten_methods.aten_meth_squeeze(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, dim: int | None = None) str[source]#

squeeze.

yobx.torch.interpreter._aten_methods.aten_meth_sum(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str, axis: str | None = None, keepdim: bool = False, dim: int | None = None) str[source]#

reducesum.

yobx.torch.interpreter._aten_methods.aten_meth_t(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

transpose

yobx.torch.interpreter._aten_methods.aten_meth_tanh(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], x: str) str[source]#

tanh.

yobx.torch.interpreter._aten_methods.aten_meth_to(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], input_name: str, *args: List[Any], name: str = '.to', **kwargs: Dict[str, Any]) str[source]#

cast

yobx.torch.interpreter._aten_methods.aten_meth_transpose(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], input_name: str, dim0: int, dim1: int) str[source]#

transpose

yobx.torch.interpreter._aten_methods.aten_meth_unsqueeze(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], input_name: str, dim: int) str[source]#

unsqueeze

yobx.torch.interpreter._aten_methods.aten_meth_view(g: GraphBuilder, sts: Dict[str, Any] | None, outputs: List[str], input_name: str, *args: Sequence[int]) str[source]#

view