# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/cv/ccvh5pcjc57oskvep5v2okylauufvnesitt2j6dbyvr6fh3vwuer.py
# Topologically Sorted Source Nodes: [output], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   output => mul_2
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 1.0), kwargs = {})
triton_poi_fused_mul_0 = async_compile.triton('triton_poi_fused_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/fz/cfzxcyhkmrsdm5u4bef4furnodily6f7e3cway2qcmgx7glijwgc.py
# Topologically Sorted Source Nodes: [output, gt, mul, pow_1, sub, clamp, sine, mul_1, phi, sub_2, phi_1, getitem, setitem], Original ATen: [aten.mul, aten.gt, aten.pow, aten.rsub, aten.clamp, aten.sqrt, aten.sub, aten.where, aten.index, aten.index_put]
# Source node to ATen node mapping:
#   clamp => clamp_max, clamp_min
#   getitem => index
#   gt => gt
#   mul => mul
#   mul_1 => mul_1
#   output => mul_2
#   phi => sub_1
#   phi_1 => where
#   pow_1 => pow_1
#   setitem => index_put
#   sine => sqrt
#   sub => sub
#   sub_2 => sub_2
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 1.0), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%arg0_1, -0.8775825618903726), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 0.8775825618903728), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %pow_1), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub, 0), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%clamp_max,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sqrt, 0.479425538604203), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, 0.23971276930210156), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %sub_1, %sub_2), kwargs = {})
#   %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%where, [%lift_fresh_copy, %arg1_1]), kwargs = {})
#   %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%mul_2, [%lift_fresh_copy_1, %arg1_1], %index), kwargs = {})
triton_poi_fused_clamp_gt_index_index_put_mul_pow_rsub_sqrt_sub_where_1 = async_compile.triton('triton_poi_fused_clamp_gt_index_index_put_mul_pow_rsub_sqrt_sub_where_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_gt_index_index_put_mul_pow_rsub_sqrt_sub_where_1', 'mutated_arg_names': ['out_ptr1'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clamp_gt_index_index_put_mul_pow_rsub_sqrt_sub_where_1(in_ptr0, in_ptr1, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16)
    x0 = xindex % 16
    x2 = xindex
    tmp11 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp0 = x1
    tmp1 = tl.full([1], 2, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 0, tl.int64)
    tmp6 = tl.where(tmp4, tmp5, tmp3)
    tmp7 = tl.full([1], 3, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.where(tmp8, tmp1, tmp7)
    tmp10 = tl.where(tmp2, tmp6, tmp9)
    tmp12 = tl.full([XBLOCK], 4, tl.int32)
    tmp13 = tmp11 + tmp12
    tmp14 = tmp11 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp11)
    tl.device_assert(((0 <= tmp15) & (tmp15 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp15 < 4")
    tmp17 = tl.load(in_ptr1 + (x0 + (16*tmp15) + (64*tmp10)), xmask)
    tmp18 = -0.8775825618903726
    tmp19 = tmp17 > tmp18
    tmp20 = 0.8775825618903728
    tmp21 = tmp17 * tmp20
    tmp22 = tmp17 * tmp17
    tmp23 = 1.0
    tmp24 = tmp23 - tmp22
    tmp25 = 0.0
    tmp26 = triton_helpers.maximum(tmp24, tmp25)
    tmp27 = triton_helpers.minimum(tmp26, tmp23)
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = 0.479425538604203
    tmp30 = tmp28 * tmp29
    tmp31 = tmp21 - tmp30
    tmp32 = 0.23971276930210156
    tmp33 = tmp17 - tmp32
    tmp34 = tl.where(tmp19, tmp31, tmp33)
    tl.store(out_ptr1 + (x0 + (16*tmp15) + (64*tmp10)), tmp34, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/sj/csjvrzgejwjlion5dslc3ka7v7ir7aoe2ycuonqyyicqmbkmudnz.py
# Topologically Sorted Source Nodes: [mul_3], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul_3 => mul_3
# Graph fragment:
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%index_put, 64.0), kwargs = {})
triton_poi_fused_mul_2 = async_compile.triton('triton_poi_fused_mul_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 64.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_0.run(arg0_1, buf1, 256, grid=grid(256), stream=stream0)
        # Topologically Sorted Source Nodes: [output, gt, mul, pow_1, sub, clamp, sine, mul_1, phi, sub_2, phi_1, getitem, setitem], Original ATen: [aten.mul, aten.gt, aten.pow, aten.rsub, aten.clamp, aten.sqrt, aten.sub, aten.where, aten.index, aten.index_put]
        triton_poi_fused_clamp_gt_index_index_put_mul_pow_rsub_sqrt_sub_where_1.run(arg1_1, arg0_1, buf1, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_3], Original ATen: [aten.mul]
        triton_poi_fused_mul_2.run(buf1, buf3, 256, grid=grid(256), stream=stream0)
        del buf1
    return (buf3, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int64)
    fn = lambda: call([arg0_1, arg1_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import math
import torch
from itertools import product as product
import torch.nn as nn
import torch.utils.data.distributed


class ArcFace(nn.Module):

    def __init__(self, s=64.0, m=0.5):
        """ArcFace formula:
            cos(m + theta) = cos(m)cos(theta) - sin(m)sin(theta)
        Note that:
            0 <= m + theta <= Pi
        So if (m + theta) >= Pi, then theta >= Pi - m. In [0, Pi]
        we have:
            cos(theta) < cos(Pi - m)
        So we can use cos(Pi - m) as threshold to check whether
        (m + theta) go out of [0, Pi]
        Args:
            embedding_size: usually 128, 256, 512 ...
            class_num: num of people when training
            s: scale, see normface https://arxiv.org/abs/1704.06369
            m: margin, see SphereFace, CosFace, and ArcFace paper
        https://github.com/siriusdemon/Build-Your-Own-Face-Model/blob/master/recognition/model/metric.py
        """
        super().__init__()
        self.s = s
        self.m = m
        self.cos_m = math.cos(m)
        self.sin_m = math.sin(m)
        self.th = math.cos(math.pi - m)
        self.mm = math.sin(math.pi - m) * m

    def forward(self, cosine, label):
        sine = (1.0 - cosine.pow(2)).clamp(0, 1).sqrt()
        phi = cosine * self.cos_m - sine * self.sin_m
        phi = torch.where(cosine > self.th, phi, cosine - self.mm)
        output = cosine * 1.0
        batch_size = len(output)
        output[range(batch_size), label] = phi[range(batch_size), label]
        return output * self.s


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.ones([4], dtype=torch.int64)]


def get_init_inputs():
    return [[], {}]

import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import math
from itertools import product as product
import torch.nn as nn
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_clamp_gt_index_index_put_mul_pow_rsub_sqrt_sub_where_1(
    in_ptr0, in_ptr1, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16
    x0 = xindex % 16
    tmp11 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp0 = x1
    tmp1 = tl.full([1], 2, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 0, tl.int64)
    tmp6 = tl.where(tmp4, tmp5, tmp3)
    tmp7 = tl.full([1], 3, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.where(tmp8, tmp1, tmp7)
    tmp10 = tl.where(tmp2, tmp6, tmp9)
    tmp12 = tl.full([XBLOCK], 4, tl.int32)
    tmp13 = tmp11 + tmp12
    tmp14 = tmp11 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp11)
    tl.device_assert((0 <= tmp15) & (tmp15 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp15 < 4')
    tmp17 = tl.load(in_ptr1 + (x0 + 16 * tmp15 + 64 * tmp10), xmask)
    tmp18 = -0.8775825618903726
    tmp19 = tmp17 > tmp18
    tmp20 = 0.8775825618903728
    tmp21 = tmp17 * tmp20
    tmp22 = tmp17 * tmp17
    tmp23 = 1.0
    tmp24 = tmp23 - tmp22
    tmp25 = 0.0
    tmp26 = triton_helpers.maximum(tmp24, tmp25)
    tmp27 = triton_helpers.minimum(tmp26, tmp23)
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = 0.479425538604203
    tmp30 = tmp28 * tmp29
    tmp31 = tmp21 - tmp30
    tmp32 = 0.23971276930210156
    tmp33 = tmp17 - tmp32
    tmp34 = tl.where(tmp19, tmp31, tmp33)
    tl.store(out_ptr1 + (x0 + 16 * tmp15 + 64 * tmp10), tmp34, xmask)


@triton.jit
def triton_poi_fused_mul_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 64.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(256)](arg0_1, buf1, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        triton_poi_fused_clamp_gt_index_index_put_mul_pow_rsub_sqrt_sub_where_1[
            grid(64)](arg1_1, arg0_1, buf1, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del arg0_1
        del arg1_1
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_2[grid(256)](buf1, buf3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf1
    return buf3,


class ArcFaceNew(nn.Module):

    def __init__(self, s=64.0, m=0.5):
        """ArcFace formula:
            cos(m + theta) = cos(m)cos(theta) - sin(m)sin(theta)
        Note that:
            0 <= m + theta <= Pi
        So if (m + theta) >= Pi, then theta >= Pi - m. In [0, Pi]
        we have:
            cos(theta) < cos(Pi - m)
        So we can use cos(Pi - m) as threshold to check whether
        (m + theta) go out of [0, Pi]
        Args:
            embedding_size: usually 128, 256, 512 ...
            class_num: num of people when training
            s: scale, see normface https://arxiv.org/abs/1704.06369
            m: margin, see SphereFace, CosFace, and ArcFace paper
        https://github.com/siriusdemon/Build-Your-Own-Face-Model/blob/master/recognition/model/metric.py
        """
        super().__init__()
        self.s = s
        self.m = m
        self.cos_m = math.cos(m)
        self.sin_m = math.sin(m)
        self.th = math.cos(math.pi - m)
        self.mm = math.sin(math.pi - m) * m

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
