__init__.py

from flag_gems.ops._functional_sym_constrain_range_for_size import (
    _functional_sym_constrain_range_for_size,
)
from flag_gems.ops._is_all_true import _is_all_true
from flag_gems.ops._safe_softmax import _safe_softmax
from flag_gems.ops._upsample_nearest_exact1d import _upsample_nearest_exact1d
from flag_gems.ops.abs import abs, abs_
from flag_gems.ops.absolute import absolute
from flag_gems.ops.acos import acos
from flag_gems.ops.act_quant import act_quant_triton
from flag_gems.ops.add import add, add_
from flag_gems.ops.addcdiv import addcdiv, addcdiv_out
from flag_gems.ops.addcmul import addcmul, addcmul_out
from flag_gems.ops.addmm import addmm, addmm_dtype, addmm_dtype_out, addmm_out
from flag_gems.ops.addmv import addmv, addmv_out
from flag_gems.ops.addr import addr
from flag_gems.ops.alias_copy import alias_copy, alias_copy_out
from flag_gems.ops.all import all, all_dim, all_dims
from flag_gems.ops.amax import amax
from flag_gems.ops.aminmax import aminmax
from flag_gems.ops.angle import angle
from flag_gems.ops.any import any, any_dim, any_dims
from flag_gems.ops.arange import arange, arange_start
from flag_gems.ops.arcsinh import arcsinh, arcsinh_out
from flag_gems.ops.arcsinh_ import arcsinh_
from flag_gems.ops.arctanh_ import arctanh_
from flag_gems.ops.argmax import argmax
from flag_gems.ops.argmin import argmin
from flag_gems.ops.asinh import asinh, asinh_out
from flag_gems.ops.asinh_ import asinh_
from flag_gems.ops.assert_async import _assert_async
from flag_gems.ops.atan import atan, atan_
from flag_gems.ops.atan2 import atan2, atan2_out
from flag_gems.ops.attention import (
    ScaleDotProductAttention,
    flash_attention_forward,
    flash_attn_varlen_func,
    flash_attn_varlen_opt_func,
    scaled_dot_product_attention,
    scaled_dot_product_attention_backward,
    scaled_dot_product_attention_forward,
)
from flag_gems.ops.avg_pool2d import avg_pool2d, avg_pool2d_backward
from flag_gems.ops.avg_pool3d import avg_pool3d, avg_pool3d_backward
from flag_gems.ops.baddbmm import baddbmm, baddbmm_out
from flag_gems.ops.batch_norm import batch_norm, batch_norm_backward
from flag_gems.ops.bernoulli_ import bernoulli_
from flag_gems.ops.bitwise_and import (
    bitwise_and_scalar,
    bitwise_and_scalar_,
    bitwise_and_scalar_tensor,
    bitwise_and_tensor,
    bitwise_and_tensor_,
)
from flag_gems.ops.bitwise_left_shift import bitwise_left_shift
from flag_gems.ops.bitwise_not import bitwise_not, bitwise_not_
from flag_gems.ops.bitwise_or import (
    bitwise_or_scalar,
    bitwise_or_scalar_,
    bitwise_or_scalar_tensor,
    bitwise_or_tensor,
    bitwise_or_tensor_,
)
from flag_gems.ops.bitwise_right_shift import bitwise_right_shift
from flag_gems.ops.bmm import bmm, bmm_out
from flag_gems.ops.cat import cat, cat_out
from flag_gems.ops.ceil import ceil, ceil_, ceil_out
from flag_gems.ops.celu import celu, celu_
from flag_gems.ops.clamp import (
    clamp,
    clamp_,
    clamp_min,
    clamp_min_,
    clamp_tensor,
    clamp_tensor_,
)
from flag_gems.ops.clip import clip, clip_
from flag_gems.ops.conj_physical import conj_physical
from flag_gems.ops.contiguous import contiguous
from flag_gems.ops.conv1d import conv1d
from flag_gems.ops.conv2d import conv2d
from flag_gems.ops.conv3d import conv3d
from flag_gems.ops.conv_depthwise2d import _conv_depthwise2d
from flag_gems.ops.copy import copy, copy_
from flag_gems.ops.copysign import copysign, copysign_out
from flag_gems.ops.cos import cos, cos_
from flag_gems.ops.cosh import cosh, cosh_, cosh_out
from flag_gems.ops.count_nonzero import count_nonzero
from flag_gems.ops.cummax import cummax
from flag_gems.ops.cummin import cummin
from flag_gems.ops.cumsum import cumsum, cumsum_out, normed_cumsum
from flag_gems.ops.diag import diag
from flag_gems.ops.diag_embed import diag_embed
from flag_gems.ops.diagonal import diagonal_backward
from flag_gems.ops.digamma_ import digamma_
from flag_gems.ops.div import (
    div_mode,
    div_mode_,
    floor_divide,
    floor_divide_,
    remainder,
    remainder_,
    true_divide,
    true_divide_,
    true_divide_out,
)
from flag_gems.ops.dot import dot
from flag_gems.ops.dropout import dropout, dropout_backward
from flag_gems.ops.einsum import einsum
from flag_gems.ops.elu import elu, elu_, elu_backward
from flag_gems.ops.embedding import embedding, embedding_backward
from flag_gems.ops.embedding_dense_backward import embedding_dense_backward
from flag_gems.ops.eq import eq, eq_scalar, equal
from flag_gems.ops.erf import erf, erf_
from flag_gems.ops.exp import exp, exp_, exp_out
from flag_gems.ops.exp2 import exp2, exp2_
from flag_gems.ops.expm1 import expm1, expm1_, expm1_out
from flag_gems.ops.exponential_ import exponential_
from flag_gems.ops.eye import eye
from flag_gems.ops.eye_m import eye_m
from flag_gems.ops.fill import (
    fill_scalar,
    fill_scalar_,
    fill_scalar_out,
    fill_tensor,
    fill_tensor_,
    fill_tensor_out,
)
from flag_gems.ops.flip import flip
from flag_gems.ops.floor_ import floor_
from flag_gems.ops.fmin import fmin, fmin_out
from flag_gems.ops.fp8_matmul import fp8_matmul
from flag_gems.ops.full import full
from flag_gems.ops.full_like import full_like
from flag_gems.ops.gather import gather, gather_backward
from flag_gems.ops.gcd import gcd, gcd_out
from flag_gems.ops.ge import ge, ge_scalar
from flag_gems.ops.gelu import gelu, gelu_, gelu_backward
from flag_gems.ops.get_paged_mqa_logits_metadata import get_paged_mqa_logits_metadata
from flag_gems.ops.get_scheduler_metadata import get_scheduler_metadata
from flag_gems.ops.glu import glu, glu_backward
from flag_gems.ops.greater import (
    greater,
    greater_out,
    greater_scalar,
    greater_scalar_out,
)
from flag_gems.ops.grid_sample import grid_sample
from flag_gems.ops.group_gemm import group_mm
from flag_gems.ops.groupnorm import group_norm, group_norm_backward
from flag_gems.ops.gt import gt, gt_scalar
from flag_gems.ops.hadamard_transform import hadamard_transform
from flag_gems.ops.hardsigmoid import hardsigmoid, hardsigmoid_out
from flag_gems.ops.hardswish_ import hardswish_
from flag_gems.ops.hstack import hstack
from flag_gems.ops.hypot import hypot, hypot_out
from flag_gems.ops.i0 import i0, i0_out
from flag_gems.ops.i0_ import i0_
from flag_gems.ops.index import index
from flag_gems.ops.index_add import index_add, index_add_
from flag_gems.ops.index_put import _index_put_impl_, index_put, index_put_
from flag_gems.ops.index_select import index_select
from flag_gems.ops.isclose import allclose, isclose
from flag_gems.ops.isfinite import isfinite
from flag_gems.ops.isin import isin
from flag_gems.ops.isinf import isinf
from flag_gems.ops.isnan import isnan
from flag_gems.ops.isneginf import isneginf, isneginf_out
from flag_gems.ops.kron import kron
from flag_gems.ops.layernorm import layer_norm, layer_norm_backward
from flag_gems.ops.le import le, le_scalar
from flag_gems.ops.leaky_relu import leaky_relu, leaky_relu_, leaky_relu_out
from flag_gems.ops.lerp import lerp_scalar, lerp_scalar_, lerp_tensor, lerp_tensor_
from flag_gems.ops.lift_fresh_copy import lift_fresh_copy, lift_fresh_copy_out
from flag_gems.ops.linspace import linspace
from flag_gems.ops.log import log
from flag_gems.ops.log1p_ import log1p_
from flag_gems.ops.log10 import log10, log10_, log10_out
from flag_gems.ops.log_sigmoid import log_sigmoid
from flag_gems.ops.log_softmax import (
    log_softmax,
    log_softmax_backward,
    log_softmax_backward_out,
    log_softmax_out,
)
from flag_gems.ops.logaddexp import logaddexp, logaddexp_out
from flag_gems.ops.logical_and import logical_and, logical_and_
from flag_gems.ops.logical_not import logical_not
from flag_gems.ops.logical_or import logical_or, logical_or_
from flag_gems.ops.logical_xor import logical_xor
from flag_gems.ops.logit import logit, logit_out
from flag_gems.ops.logit_ import logit_
from flag_gems.ops.logspace import logspace
from flag_gems.ops.lt import lt, lt_scalar
from flag_gems.ops.margin_ranking_loss import margin_ranking_loss
from flag_gems.ops.masked_fill import masked_fill, masked_fill_
from flag_gems.ops.masked_scatter import masked_scatter, masked_scatter_
from flag_gems.ops.masked_select import masked_select
from flag_gems.ops.max import max, max_dim
from flag_gems.ops.max_pool2d_with_indices import (
    max_pool2d_backward,
    max_pool2d_with_indices,
)
from flag_gems.ops.max_pool3d_with_indices import (
    max_pool3d_backward,
    max_pool3d_with_indices,
)
from flag_gems.ops.maximum import maximum
from flag_gems.ops.mean import mean, mean_dim
from flag_gems.ops.min import min, min_dim
from flag_gems.ops.minimum import minimum
from flag_gems.ops.mm import mm, mm_out
from flag_gems.ops.mse_loss import mse_loss
from flag_gems.ops.mul import mul, mul_
from flag_gems.ops.multinomial import multinomial
from flag_gems.ops.mv import mv
from flag_gems.ops.nan_to_num import nan_to_num
from flag_gems.ops.ne import ne, ne_scalar
from flag_gems.ops.neg import neg, neg_
from flag_gems.ops.new_full import new_full
from flag_gems.ops.nll_loss_nd import nll_loss_nd_backward, nll_loss_nd_forward
from flag_gems.ops.nllloss import (
    nll_loss2d_backward,
    nll_loss2d_forward,
    nll_loss_backward,
    nll_loss_forward,
)
from flag_gems.ops.nonzero import nonzero
from flag_gems.ops.normal import (
    normal_,
    normal_float_tensor,
    normal_tensor_float,
    normal_tensor_tensor,
)
from flag_gems.ops.one_hot import one_hot
from flag_gems.ops.ones import ones
from flag_gems.ops.ones_like import ones_like
from flag_gems.ops.pad import constant_pad_nd, pad
from flag_gems.ops.per_token_group_quant_fp8 import (
    SUPPORTED_FP8_DTYPE,
    per_token_group_quant_fp8,
)
from flag_gems.ops.pixel_shuffle import pixel_shuffle
from flag_gems.ops.pixel_unshuffle import pixel_unshuffle, pixel_unshuffle_out
from flag_gems.ops.polar import polar
from flag_gems.ops.pow import (
    pow_scalar,
    pow_tensor_scalar,
    pow_tensor_scalar_,
    pow_tensor_tensor,
    pow_tensor_tensor_,
)
from flag_gems.ops.prelu import prelu
from flag_gems.ops.prod import prod, prod_dim
from flag_gems.ops.quantile import quantile
from flag_gems.ops.rand import rand
from flag_gems.ops.rand_like import rand_like
from flag_gems.ops.randn import randn
from flag_gems.ops.randn_like import randn_like
from flag_gems.ops.randperm import randperm
from flag_gems.ops.reciprocal import reciprocal, reciprocal_
from flag_gems.ops.reflection_pad1d import reflection_pad1d, reflection_pad1d_out
from flag_gems.ops.reflection_pad2d import reflection_pad2d, reflection_pad2d_out
from flag_gems.ops.relu import relu, relu_
from flag_gems.ops.relu6 import relu6
from flag_gems.ops.repeat import repeat
from flag_gems.ops.repeat_interleave import (
    repeat_interleave_self_int,
    repeat_interleave_self_tensor,
    repeat_interleave_tensor,
)
from flag_gems.ops.replication_pad1d import replication_pad1d, replication_pad1d_out
from flag_gems.ops.replication_pad3d import replication_pad3d
from flag_gems.ops.resolve_conj import resolve_conj
from flag_gems.ops.resolve_neg import resolve_neg
from flag_gems.ops.rms_norm import rms_norm, rms_norm_backward, rms_norm_forward
from flag_gems.ops.roll import roll
from flag_gems.ops.round import round, round_, round_out
from flag_gems.ops.rrelu_with_noise_backward import rrelu_with_noise_backward
from flag_gems.ops.rsqrt import rsqrt, rsqrt_
from flag_gems.ops.scaled_softmax import scaled_softmax_backward, scaled_softmax_forward
from flag_gems.ops.scatter import scatter, scatter_
from flag_gems.ops.scatter_add_ import scatter_add_
from flag_gems.ops.scatter_reduce_ import scatter_reduce_
from flag_gems.ops.select_backward import select_backward
from flag_gems.ops.select_scatter import select_scatter
from flag_gems.ops.selu import selu
from flag_gems.ops.selu_ import selu_
from flag_gems.ops.sgn_ import sgn_
from flag_gems.ops.sigmoid import sigmoid, sigmoid_, sigmoid_backward
from flag_gems.ops.signbit import signbit, signbit_out
from flag_gems.ops.silu import silu, silu_, silu_backward
from flag_gems.ops.sin import sin, sin_
from flag_gems.ops.sinh_ import sinh_
from flag_gems.ops.slice_backward import slice_backward
from flag_gems.ops.slice_scatter import slice_scatter
from flag_gems.ops.soft_margin_loss import soft_margin_loss, soft_margin_loss_out
from flag_gems.ops.softmax import (
    softmax,
    softmax_backward,
    softmax_backward_out,
    softmax_out,
)
from flag_gems.ops.softplus import softplus
from flag_gems.ops.softshrink import softshrink, softshrink_out
from flag_gems.ops.sort import sort, sort_stable
from flag_gems.ops.special_i0e import special_i0e, special_i0e_out
from flag_gems.ops.special_i1 import special_i1, special_i1_out
from flag_gems.ops.sqrt import sqrt, sqrt_
from flag_gems.ops.square import square, square_, square_out
from flag_gems.ops.stack import stack
from flag_gems.ops.std import std
from flag_gems.ops.sub import sub, sub_
from flag_gems.ops.sum import sum, sum_dim, sum_dim_out, sum_out
from flag_gems.ops.t_copy import t_copy, t_copy_out
from flag_gems.ops.tan import tan, tan_
from flag_gems.ops.tanh import tanh, tanh_, tanh_backward
from flag_gems.ops.threshold import threshold, threshold_backward
from flag_gems.ops.tile import tile
from flag_gems.ops.to import to_copy
from flag_gems.ops.topk import topk
from flag_gems.ops.trace import trace
from flag_gems.ops.tril import tril, tril_out
from flag_gems.ops.triu import triu, triu_
from flag_gems.ops.unfold_backward import unfold_backward
from flag_gems.ops.uniform import uniform_
from flag_gems.ops.unique import _unique2
from flag_gems.ops.unique_consecutive import unique_consecutive
from flag_gems.ops.upsample_bicubic2d import upsample_bicubic2d
from flag_gems.ops.upsample_bicubic2d_aa import _upsample_bicubic2d_aa
from flag_gems.ops.upsample_bicubic2d_aa_backward import _upsample_bicubic2d_aa_backward
from flag_gems.ops.upsample_linear1d import upsample_linear1d
from flag_gems.ops.upsample_nearest1d import upsample_nearest1d
from flag_gems.ops.upsample_nearest2d import upsample_nearest2d
from flag_gems.ops.upsample_nearest3d import upsample_nearest3d
from flag_gems.ops.var import var, var_correction, var_dim
from flag_gems.ops.var_mean import var_mean
from flag_gems.ops.vdot import vdot
from flag_gems.ops.vector_norm import vector_norm
from flag_gems.ops.vstack import vstack
from flag_gems.ops.w8a8_block_fp8_matmul import w8a8_block_fp8_matmul
from flag_gems.ops.weightnorm import (
    weight_norm_interface,
    weight_norm_interface_backward,
)
from flag_gems.ops.where import (
    where_scalar_other,
    where_scalar_self,
    where_self,
    where_self_out,
)
from flag_gems.ops.zero import zero, zero_out
from flag_gems.ops.zeros import zero_, zeros
from flag_gems.ops.zeros_like import zeros_like

__all__ = [
    "_assert_async",
    "_conv_depthwise2d",
    "_functional_sym_constrain_range_for_size",
    "_index_put_impl_",
    "_is_all_true",
    "_safe_softmax",
    "_unique2",
    "_upsample_bicubic2d_aa",
    "_upsample_bicubic2d_aa_backward",
    "_upsample_nearest_exact1d",
    "abs",
    "abs_",
    "absolute",
    "act_quant_triton",
    "acos",
    "add",
    "add_",
    "addcdiv",
    "addcdiv_out",
    "addcmul",
    "addcmul_out",
    "addmm",
    "addmm_dtype",
    "addmm_dtype_out",
    "addmm_out",
    "addmv",
    "addmv_out",
    "addr",
    "alias_copy",
    "alias_copy_out",
    "all",
    "all_dim",
    "all_dims",
    "allclose",
    "amax",
    "aminmax",
    "angle",
    "any",
    "any_dim",
    "any_dims",
    "arange",
    "arange_start",
    "arcsinh",
    "arcsinh_out",
    "arctanh_",
    "arcsinh_",
    "argmax",
    "argmin",
    "asinh",
    "asinh_",
    "asinh_out",
    "atan",
    "atan_",
    "atan2",
    "atan2_out",
    "avg_pool2d",
    "avg_pool2d_backward",
    "avg_pool3d",
    "avg_pool3d_backward",
    "baddbmm",
    "baddbmm_out",
    "batch_norm",
    "batch_norm_backward",
    "bernoulli_",
    "bitwise_and_scalar",
    "bitwise_and_scalar_",
    "bitwise_and_scalar_tensor",
    "bitwise_and_tensor",
    "bitwise_and_tensor_",
    "bitwise_left_shift",
    "bitwise_not",
    "bitwise_not_",
    "bitwise_or_scalar",
    "bitwise_or_scalar_",
    "bitwise_or_scalar_tensor",
    "bitwise_or_tensor",
    "bitwise_or_tensor_",
    "bitwise_right_shift",
    "bmm",
    "bmm_out",
    "cat",
    "cat_out",
    "ceil",
    "ceil_",
    "ceil_out",
    "celu",
    "celu_",
    "clamp",
    "clamp_",
    "clamp_min",
    "clamp_min_",
    "clamp_tensor",
    "clamp_tensor_",
    "clip",
    "clip_",
    "constant_pad_nd",
    "contiguous",
    "conv1d",
    "conv2d",
    "conv3d",
    "copy",
    "copy_",
    "copysign",
    "copysign_out",
    "cos",
    "cos_",
    "cosh",
    "cosh_",
    "cosh_out",
    "count_nonzero",
    "cummax",
    "cummin",
    "cumsum",
    "cumsum_out",
    "conj_physical",
    "diag",
    "diag_embed",
    "diagonal_backward",
    "digamma_",
    "div_mode",
    "div_mode_",
    "dot",
    "dropout",
    "dropout_backward",
    "einsum",
    "elu",
    "elu_",
    "elu_backward",
    "embedding",
    "embedding_backward",
    "embedding_dense_backward",
    "eq",
    "eq_scalar",
    "equal",
    "erf",
    "erf_",
    "exp",
    "exp_",
    "exp_out",
    "exp2",
    "exp2_",
    "expm1",
    "expm1_",
    "expm1_out",
    "exponential_",
    "eye",
    "eye_m",
    "fill_scalar",
    "fill_scalar_",
    "fill_scalar_out",
    "fill_tensor",
    "fill_tensor_",
    "fill_tensor_out",
    "flash_attention_forward",
    "flash_attn_varlen_func",
    "flash_attn_varlen_opt_func",
    "flip",
    "floor_",
    "floor_divide",
    "floor_divide_",
    "fmin",
    "fmin_out",
    "full",
    "full_like",
    "gather",
    "gather_backward",
    "gcd",
    "gcd_out",
    "ge",
    "ge_scalar",
    "gelu",
    "gelu_",
    "gelu_backward",
    "get_paged_mqa_logits_metadata",
    "get_scheduler_metadata",
    "glu",
    "glu_backward",
    "grid_sample",
    "greater",
    "greater_out",
    "greater_scalar",
    "greater_scalar_out",
    "group_mm",
    "group_norm",
    "group_norm_backward",
    "gt",
    "gt_scalar",
    "hadamard_transform",
    "hardsigmoid",
    "hardsigmoid_out",
    "hardswish_",
    "hstack",
    "hypot",
    "hypot_out",
    "i0",
    "i0_out",
    "i0_",
    "index",
    "index_add",
    "index_add_",
    "index_put",
    "index_put_",
    "index_select",
    "isclose",
    "isfinite",
    "isin",
    "isinf",
    "isnan",
    "isneginf",
    "isneginf_out",
    "kron",
    "layer_norm",
    "layer_norm_backward",
    "leaky_relu",
    "leaky_relu_",
    "leaky_relu_out",
    "le",
    "le_scalar",
    "lerp_scalar",
    "lerp_scalar_",
    "lerp_tensor",
    "lerp_tensor_",
    "lift_fresh_copy",
    "lift_fresh_copy_out",
    "linspace",
    "log",
    "log10",
    "log10_",
    "log10_out",
    "log_sigmoid",
    "log_softmax",
    "log_softmax_backward",
    "log_softmax_backward_out",
    "log_softmax_out",
    "log1p_",
    "logaddexp",
    "logaddexp_out",
    "logical_and",
    "logical_and_",
    "logical_not",
    "logical_or",
    "logical_or_",
    "logical_xor",
    "logit",
    "logit_out",
    "logit_",
    "logspace",
    "lt",
    "lt_scalar",
    "margin_ranking_loss",
    "masked_fill",
    "masked_fill_",
    "masked_scatter",
    "masked_scatter_",
    "masked_select",
    "max",
    "max_dim",
    "max_pool2d_with_indices",
    "max_pool2d_backward",
    "max_pool3d_with_indices",
    "max_pool3d_backward",
    "maximum",
    "mean",
    "mean_dim",
    "min",
    "min_dim",
    "minimum",
    "mm",
    "mm_out",
    "mse_loss",
    "mul",
    "mul_",
    "multinomial",
    "mv",
    "nan_to_num",
    "ne",
    "ne_scalar",
    "neg",
    "neg_",
    "new_full",
    "nll_loss_backward",
    "nll_loss_forward",
    "nll_loss2d_backward",
    "nll_loss2d_forward",
    "nll_loss_nd_forward",
    "nll_loss_nd_backward",
    "nonzero",
    "normal_float_tensor",
    "normal_tensor_float",
    "normal_tensor_tensor",
    "normal_",
    "normed_cumsum",
    "ones",
    "ones_like",
    "one_hot",
    "pad",
    "per_token_group_quant_fp8",
    "pixel_shuffle",
    "pixel_unshuffle",
    "pixel_unshuffle_out",
    "polar",
    "pow_scalar",
    "pow_tensor_scalar",
    "pow_tensor_scalar_",
    "pow_tensor_tensor",
    "pow_tensor_tensor_",
    "prelu",
    "prod",
    "prod_dim",
    "quantile",
    "rand",
    "rand_like",
    "randn",
    "randn_like",
    "randperm",
    "reciprocal",
    "reciprocal_",
    "reflection_pad2d",
    "reflection_pad2d_out",
    "reflection_pad1d",
    "reflection_pad1d_out",
    "relu",
    "relu_",
    "relu6",
    "remainder",
    "remainder_",
    "repeat",
    "repeat_interleave_self_int",
    "repeat_interleave_self_tensor",
    "repeat_interleave_tensor",
    "replication_pad1d",
    "replication_pad1d_out",
    "replication_pad3d",
    "resolve_conj",
    "resolve_neg",
    "rms_norm",
    "rms_norm_backward",
    "rms_norm_forward",
    "roll",
    "round",
    "round_",
    "round_out",
    "rrelu_with_noise_backward",
    "rsqrt",
    "rsqrt_",
    "scaled_dot_product_attention",
    "scaled_dot_product_attention_backward",
    "scaled_dot_product_attention_forward",
    "scaled_softmax_backward",
    "scaled_softmax_forward",
    "scatter",
    "scatter_",
    "scatter_add_",
    "scatter_reduce_",
    "select_backward",
    "select_scatter",
    "selu",
    "selu_",
    "sgn_",
    "sigmoid",
    "sigmoid_",
    "sigmoid_backward",
    "signbit",
    "signbit_out",
    "silu",
    "silu_",
    "silu_backward",
    "sin",
    "sin_",
    "sinh_",
    "slice_backward",
    "slice_scatter",
    "soft_margin_loss",
    "soft_margin_loss_out",
    "softmax",
    "softmax_backward",
    "softmax_backward_out",
    "softmax_out",
    "softplus",
    "softshrink",
    "softshrink_out",
    "sort",
    "sort_stable",
    "special_i1",
    "special_i1_out",
    "special_i0e",
    "special_i0e_out",
    "sqrt",
    "sqrt_",
    "square",
    "square_",
    "square_out",
    "stack",
    "std",
    "sub",
    "sub_",
    "sum",
    "sum_dim",
    "sum_dim_out",
    "sum_out",
    "ScaleDotProductAttention",
    "SUPPORTED_FP8_DTYPE",
    "t_copy",
    "t_copy_out",
    "tan",
    "tan_",
    "tanh",
    "tanh_",
    "tanh_backward",
    "threshold",
    "threshold_backward",
    "tile",
    "to_copy",
    "topk",
    "trace",
    "tril",
    "tril_out",
    "triu",
    "triu_",
    "true_divide",
    "true_divide_",
    "true_divide_out",
    "unfold_backward",
    "uniform_",
    "unique_consecutive",
    "upsample_bicubic2d",
    "upsample_linear1d",
    "upsample_nearest1d",
    "upsample_nearest2d",
    "upsample_nearest3d",
    "var_mean",
    "var",
    "var_correction",
    "var_dim",
    "vdot",
    "vector_norm",
    "vstack",
    "fp8_matmul",
    "w8a8_block_fp8_matmul",
    "weight_norm_interface",
    "weight_norm_interface_backward",
    "where_scalar_other",
    "where_scalar_self",
    "where_self",
    "where_self_out",
    "zero",
    "zero_out",
    "zero_",
    "zeros",
    "zeros_like",
]