# AOT ID: ['0_forward']
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_8/inductor_cache/qs/cqs7hsmsrikfeknl7bymfogaydgm7dvytdeqjmdpl7atyde3il42.py
# Topologically Sorted Source Nodes: [bases], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   bases => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%mul, %mul_1, %mul_2, %mul_3], 2), kwargs = {})
triton_poi_fused_stack_0 = async_compile.triton('triton_poi_fused_stack_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=[1024], 
    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_stack_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_stack_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 16
    x0 = xindex % 4
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (4*x1) + (16*x2)), tmp4 & xmask, other=0.0)
    tmp6 = 1.5707963267948966
    tmp7 = tmp5 * tmp6
    tmp8 = tl_math.sin(tmp7)
    tmp9 = 0.6366197723675814
    tmp10 = tmp8 * tmp9
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp4, tmp10, tmp11)
    tmp13 = tmp0 >= tmp3
    tmp14 = tl.full([1], 8, tl.int64)
    tmp15 = tmp0 < tmp14
    tmp16 = tmp13 & tmp15
    tmp17 = tl.load(in_ptr0 + (x0 + (4*((-4) + x1)) + (16*x2)), tmp16 & xmask, other=0.0)
    tmp18 = 4.712388980384691
    tmp19 = tmp17 * tmp18
    tmp20 = tl_math.sin(tmp19)
    tmp21 = 0.21220659078919377
    tmp22 = tmp20 * tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp16, tmp22, tmp23)
    tmp25 = tmp0 >= tmp14
    tmp26 = tl.full([1], 12, tl.int64)
    tmp27 = tmp0 < tmp26
    tmp28 = tmp25 & tmp27
    tmp29 = tl.load(in_ptr0 + (x0 + (4*((-8) + x1)) + (16*x2)), tmp28 & xmask, other=0.0)
    tmp30 = 7.853981633974483
    tmp31 = tmp29 * tmp30
    tmp32 = tl_math.sin(tmp31)
    tmp33 = 0.12732395447351627
    tmp34 = tmp32 * tmp33
    tmp35 = tl.full(tmp34.shape, 0.0, tmp34.dtype)
    tmp36 = tl.where(tmp28, tmp34, tmp35)
    tmp37 = tmp0 >= tmp26
    tmp38 = tl.full([1], 16, tl.int64)
    tmp39 = tmp0 < tmp38
    tmp40 = tl.load(in_ptr0 + (x0 + (4*((-12) + x1)) + (16*x2)), tmp37 & xmask, other=0.0)
    tmp41 = 10.995574287564276
    tmp42 = tmp40 * tmp41
    tmp43 = tl_math.sin(tmp42)
    tmp44 = 0.09094568176679733
    tmp45 = tmp43 * tmp44
    tmp46 = tl.full(tmp45.shape, 0.0, tmp45.dtype)
    tmp47 = tl.where(tmp37, tmp45, tmp46)
    tmp48 = tl.where(tmp28, tmp36, tmp47)
    tmp49 = tl.where(tmp16, tmp24, tmp48)
    tmp50 = tl.where(tmp4, tmp12, tmp49)
    tl.store(out_ptr0 + (x3), tmp50, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/h2/ch2o6g52ansxwyr4x7ytrs4svgxcrxtqxbcz5if32qikgx2c2yo6.py
# Topologically Sorted Source Nodes: [x, pow_1, sum_1, truediv_4], Original ATen: [aten.sub, aten.pow, aten.sum, aten.div]
# Source node to ATen node mapping:
#   pow_1 => pow_1
#   sum_1 => sum_1
#   truediv_4 => div_4
#   x => sub
# Graph fragment:
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mm, %mm_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_1,), kwargs = {})
#   %div_4 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, 64), kwargs = {})
triton_per_fused_div_pow_sub_sum_1 = async_compile.triton('triton_per_fused_div_pow_sub_sum_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.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_pow_sub_sum_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_div_pow_sub_sum_1(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_out_ptr0 + (r0), None)
    tmp1 = tl.load(in_ptr0 + (r0), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp7 = 0.015625
    tmp8 = tmp6 * tmp7
    tl.store(in_out_ptr0 + (tl.broadcast_to(r0, [RBLOCK])), tmp2, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (tl.full([1], 0, tl.int32)), tmp8, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 16), (16, 1))
    assert_size_stride(primals_3, (4, 16), (16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 16, 4), (256, 64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [bases], Original ATen: [aten.stack]
        stream0 = get_raw_stream(0)
        triton_poi_fused_stack_0.run(primals_1, buf0, 1024, grid=grid(1024), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 16), (16, 1), 0), reinterpret_tensor(primals_2, (16, 4), (1, 16), 0), out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 16), (16, 1), 0), reinterpret_tensor(primals_3, (16, 4), (1, 16), 0), out=buf2)
        del primals_3
        buf3 = buf1; del buf1  # reuse
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [x, pow_1, sum_1, truediv_4], Original ATen: [aten.sub, aten.pow, aten.sum, aten.div]
        triton_per_fused_div_pow_sub_sum_1.run(buf3, buf5, buf2, 1, 256, grid=grid(1), stream=stream0)
        del buf2
    return (buf3, buf5, reinterpret_tensor(buf0, (64, 16), (16, 1), 0), buf3, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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
import numpy as np
import torch.nn as nn


class NotearsSobolev(nn.Module):

    def __init__(self, d, k):
        """d: num variables k: num expansion of each variable"""
        super(NotearsSobolev, self).__init__()
        self.d, self.k = d, k
        self.fc1_pos = nn.Linear(d * k, d, bias=False)
        self.fc1_neg = nn.Linear(d * k, d, bias=False)
        self.fc1_pos.weight.bounds = self._bounds()
        self.fc1_neg.weight.bounds = self._bounds()
        nn.init.zeros_(self.fc1_pos.weight)
        nn.init.zeros_(self.fc1_neg.weight)
        self.l2_reg_store = None

    def _bounds(self):
        bounds = []
        for j in range(self.d):
            for i in range(self.d):
                for _ in range(self.k):
                    if i == j:
                        bound = 0, 0
                    else:
                        bound = 0, None
                    bounds.append(bound)
        return bounds

    def sobolev_basis(self, x):
        seq = []
        for kk in range(self.k):
            mu = 2.0 / (2 * kk + 1) / math.pi
            psi = mu * torch.sin(x / mu)
            seq.append(psi)
        bases = torch.stack(seq, dim=2)
        bases = bases.view(-1, self.d * self.k)
        return bases

    def forward(self, x):
        bases = self.sobolev_basis(x)
        x = self.fc1_pos(bases) - self.fc1_neg(bases)
        self.l2_reg_store = torch.sum(x ** 2) / x.shape[0]
        return x

    def h_func(self):
        fc1_weight = self.fc1_pos.weight - self.fc1_neg.weight
        fc1_weight = fc1_weight.view(self.d, self.d, self.k)
        A = torch.sum(fc1_weight * fc1_weight, dim=2).t()
        h = trace_expm(A) - d
        return h

    def l2_reg(self):
        reg = self.l2_reg_store
        return reg

    def fc1_l1_reg(self):
        reg = torch.sum(self.fc1_pos.weight + self.fc1_neg.weight)
        return reg

    @torch.no_grad()
    def fc1_to_adj(self) ->np.ndarray:
        fc1_weight = self.fc1_pos.weight - self.fc1_neg.weight
        fc1_weight = fc1_weight.view(self.d, self.d, self.k)
        A = torch.sum(fc1_weight * fc1_weight, dim=2).t()
        W = torch.sqrt(A)
        W = W.cpu().detach().numpy()
        return W


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'d': 4, 'k': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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 math as tl_math
import math
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_stack_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 16
    x0 = xindex % 4
    x2 = xindex // 64
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp4 & xmask, other=0.0)
    tmp6 = 1.5707963267948966
    tmp7 = tmp5 * tmp6
    tmp8 = tl_math.sin(tmp7)
    tmp9 = 0.6366197723675814
    tmp10 = tmp8 * tmp9
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp4, tmp10, tmp11)
    tmp13 = tmp0 >= tmp3
    tmp14 = tl.full([1], 8, tl.int64)
    tmp15 = tmp0 < tmp14
    tmp16 = tmp13 & tmp15
    tmp17 = tl.load(in_ptr0 + (x0 + 4 * (-4 + x1) + 16 * x2), tmp16 & xmask,
        other=0.0)
    tmp18 = 4.712388980384691
    tmp19 = tmp17 * tmp18
    tmp20 = tl_math.sin(tmp19)
    tmp21 = 0.21220659078919377
    tmp22 = tmp20 * tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp16, tmp22, tmp23)
    tmp25 = tmp0 >= tmp14
    tmp26 = tl.full([1], 12, tl.int64)
    tmp27 = tmp0 < tmp26
    tmp28 = tmp25 & tmp27
    tmp29 = tl.load(in_ptr0 + (x0 + 4 * (-8 + x1) + 16 * x2), tmp28 & xmask,
        other=0.0)
    tmp30 = 7.853981633974483
    tmp31 = tmp29 * tmp30
    tmp32 = tl_math.sin(tmp31)
    tmp33 = 0.12732395447351627
    tmp34 = tmp32 * tmp33
    tmp35 = tl.full(tmp34.shape, 0.0, tmp34.dtype)
    tmp36 = tl.where(tmp28, tmp34, tmp35)
    tmp37 = tmp0 >= tmp26
    tl.full([1], 16, tl.int64)
    tmp40 = tl.load(in_ptr0 + (x0 + 4 * (-12 + x1) + 16 * x2), tmp37 &
        xmask, other=0.0)
    tmp41 = 10.995574287564276
    tmp42 = tmp40 * tmp41
    tmp43 = tl_math.sin(tmp42)
    tmp44 = 0.09094568176679733
    tmp45 = tmp43 * tmp44
    tmp46 = tl.full(tmp45.shape, 0.0, tmp45.dtype)
    tmp47 = tl.where(tmp37, tmp45, tmp46)
    tmp48 = tl.where(tmp28, tmp36, tmp47)
    tmp49 = tl.where(tmp16, tmp24, tmp48)
    tmp50 = tl.where(tmp4, tmp12, tmp49)
    tl.store(out_ptr0 + x3, tmp50, xmask)


@triton.jit
def triton_per_fused_div_pow_sub_sum_1(in_out_ptr0, in_out_ptr1, in_ptr0,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_out_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr0 + r0, None)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp7 = 0.015625
    tmp8 = tmp6 * tmp7
    tl.store(in_out_ptr0 + tl.broadcast_to(r0, [RBLOCK]), tmp2, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([1], 0, tl.int32), tmp8, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 16), (16, 1))
    assert_size_stride(primals_3, (4, 16), (16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 16, 4), (256, 64, 4, 1), torch.float32
            )
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(1024)](primals_1, buf0, 1024, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 16), (16, 1), 0),
            reinterpret_tensor(primals_2, (16, 4), (1, 16), 0), out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 16), (16, 1), 0),
            reinterpret_tensor(primals_3, (16, 4), (1, 16), 0), out=buf2)
        del primals_3
        buf3 = buf1
        del buf1
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4
        del buf4
        triton_per_fused_div_pow_sub_sum_1[grid(1)](buf3, buf5, buf2, 1, 
            256, num_warps=2, num_stages=1)
        del buf2
    return buf3, buf5, reinterpret_tensor(buf0, (64, 16), (16, 1), 0), buf3


class NotearsSobolevNew(nn.Module):

    def __init__(self, d, k):
        """d: num variables k: num expansion of each variable"""
        super(NotearsSobolevNew, self).__init__()
        self.d, self.k = d, k
        self.fc1_pos = nn.Linear(d * k, d, bias=False)
        self.fc1_neg = nn.Linear(d * k, d, bias=False)
        self.fc1_pos.weight.bounds = self._bounds()
        self.fc1_neg.weight.bounds = self._bounds()
        nn.init.zeros_(self.fc1_pos.weight)
        nn.init.zeros_(self.fc1_neg.weight)
        self.l2_reg_store = None

    def _bounds(self):
        bounds = []
        for j in range(self.d):
            for i in range(self.d):
                for _ in range(self.k):
                    if i == j:
                        bound = 0, 0
                    else:
                        bound = 0, None
                    bounds.append(bound)
        return bounds

    def sobolev_basis(self, x):
        seq = []
        for kk in range(self.k):
            mu = 2.0 / (2 * kk + 1) / math.pi
            psi = mu * torch.sin(x / mu)
            seq.append(psi)
        bases = torch.stack(seq, dim=2)
        bases = bases.view(-1, self.d * self.k)
        return bases

    def h_func(self):
        fc1_weight = self.fc1_pos.weight - self.fc1_neg.weight
        fc1_weight = fc1_weight.view(self.d, self.d, self.k)
        A = torch.sum(fc1_weight * fc1_weight, dim=2).t()
        h = trace_expm(A) - d
        return h

    def l2_reg(self):
        reg = self.l2_reg_store
        return reg

    def fc1_l1_reg(self):
        reg = torch.sum(self.fc1_pos.weight + self.fc1_neg.weight)
        return reg

    @torch.no_grad()
    def fc1_to_adj(self) ->np.ndarray:
        fc1_weight = self.fc1_pos.weight - self.fc1_neg.weight
        fc1_weight = fc1_weight.view(self.d, self.d, self.k)
        A = torch.sum(fc1_weight * fc1_weight, dim=2).t()
        W = torch.sqrt(A)
        W = W.cpu().detach().numpy()
        return W

    def forward(self, input_0):
        primals_2 = self.fc1_pos.weight
        primals_3 = self.fc1_neg.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
