# 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_1/inductor_cache/ul/culh4nujulpuho3tsoabwbr3wz6pn5aakbvnlvd5bncz7lcaovvf.py
# Topologically Sorted Source Nodes: [W], Original ATen: [aten.clamp, aten.ge, aten.le, aten.logical_and]
# Source node to ATen node mapping:
#   W => clamp_max, clamp_min
# Graph fragment:
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%primals_1, -1), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1), kwargs = {})
#   %ge_1 : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%primals_1, -1), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%primals_1, 1), kwargs = {})
#   %logical_and_1 : [num_users=1] = call_function[target=torch.ops.aten.logical_and.default](args = (%ge_1, %le_1), kwargs = {})
triton_poi_fused_clamp_ge_le_logical_and_0 = async_compile.triton('triton_poi_fused_clamp_ge_le_logical_and_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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_ge_le_logical_and_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_ge_le_logical_and_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = -1.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 1.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = tmp0 >= tmp1
    tmp6 = tmp0 <= tmp3
    tmp7 = tmp5 & tmp6
    tl.store(out_ptr0 + (x0), tmp4, None)
    tl.store(out_ptr1 + (x0), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/mo/cmofqdy3de6qxndsd34hwmknhh7gg5wfepc2gayqxjjn222pf7yd.py
# Topologically Sorted Source Nodes: [gelu], Original ATen: [aten.gelu]
# Source node to ATen node mapping:
#   gelu => add, erf, mul, mul_1, mul_2
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 0.5), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 0.7071067811865476), kwargs = {})
#   %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%mul_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %add), kwargs = {})
triton_poi_fused_gelu_1 = async_compile.triton('triton_poi_fused_gelu_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_gelu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_gelu_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + (x0), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/wg/cwg64vhtivwzvl5ldyq7lxy7b3yid4k7cl2m6j2m7nditt7gz2q4.py
# Topologically Sorted Source Nodes: [gelu_1], Original ATen: [aten.gelu]
# Source node to ATen node mapping:
#   gelu_1 => add_1, erf_1, mul_3, mul_4, mul_5
# Graph fragment:
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 0.5), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 0.7071067811865476), kwargs = {})
#   %erf_1 : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%mul_4,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf_1, 1), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %add_1), kwargs = {})
triton_poi_fused_gelu_2 = async_compile.triton('triton_poi_fused_gelu_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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_gelu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_gelu_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 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', 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, (1024, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 1024), (1024, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1024, 4), (4, 1), torch.float32)
        buf7 = empty_strided_cuda((1024, 4), (4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [W], Original ATen: [aten.clamp, aten.ge, aten.le, aten.logical_and]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clamp_ge_le_logical_and_0.run(primals_1, buf0, buf7, 4096, grid=grid(4096), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((64, 1024), (1024, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(buf0, (4, 1024), (1, 4), 0), out=buf1)
        buf2 = reinterpret_tensor(buf0, (1024, 4), (1, 1024), 0); del buf0  # reuse
        buf6 = empty_strided_cuda((4, 1024), (1024, 1), torch.bool)
        # Topologically Sorted Source Nodes: [W_1, linear_1], Original ATen: [aten.clamp, aten.t, aten.ge, aten.le, aten.logical_and]
        triton_poi_fused_clamp_ge_le_logical_and_0.run(primals_3, buf2, buf6, 4096, grid=grid(4096), stream=stream0)
        del primals_3
        buf3 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1), torch.float32)
        # Topologically Sorted Source Nodes: [gelu], Original ATen: [aten.gelu]
        triton_poi_fused_gelu_1.run(buf1, buf3, 65536, grid=grid(65536), stream=stream0)
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 1024), (1024, 1), 0), buf2, out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [gelu_1], Original ATen: [aten.gelu]
        triton_poi_fused_gelu_2.run(buf4, buf5, 256, grid=grid(256), stream=stream0)
    return (buf5, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), buf1, reinterpret_tensor(buf3, (64, 1024), (1024, 1), 0), buf4, reinterpret_tensor(buf2, (4, 1024), (1024, 1), 0), buf6, buf7, )


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((1024, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1024), (1024, 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 torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler


class ReRegualizedLinearNACLayer(torch.nn.Module):

    def __init__(self, in_features, out_features, **kwargs):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.W = torch.nn.Parameter(torch.Tensor(out_features, in_features))
        self.register_parameter('bias', None)

    def reset_parameters(self):
        std = math.sqrt(2.0 / (self.in_features + self.out_features))
        r = min(0.5, math.sqrt(3.0) * std)
        torch.nn.init.uniform_(self.W, -r, r)

    def forward(self, input, reuse=False):
        W = torch.clamp(self.W, -1, 1)
        return torch.nn.functional.linear(input, W, self.bias)

    def extra_repr(self):
        return 'in_features={}, out_features={}'.format(self.in_features,
            self.out_features)


class ByteCombine(nn.Module):

    def __init__(self, input_dim, output_dim, inner_dim=1024, **kwags):
        super().__init__()
        self.layer_1 = ReRegualizedLinearNACLayer(input_dim, inner_dim)
        self.layer_2 = ReRegualizedLinearNACLayer(inner_dim, output_dim)
        self.act = nn.GELU()
        self.reset_parameters()

    def reset_parameters(self):
        self.layer_1.reset_parameters()
        self.layer_2.reset_parameters()

    def forward(self, input):
        return self.act(self.layer_2(self.act(self.layer_1(input))))


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


def get_init_inputs():
    return [[], {'input_dim': 4, 'output_dim': 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 libdevice
import math
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
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_clamp_ge_le_logical_and_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = -1.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 1.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = tmp0 >= tmp1
    tmp6 = tmp0 <= tmp3
    tmp7 = tmp5 & tmp6
    tl.store(out_ptr0 + x0, tmp4, None)
    tl.store(out_ptr1 + x0, tmp7, None)


@triton.jit
def triton_poi_fused_gelu_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + x0, tmp8, None)


@triton.jit
def triton_poi_fused_gelu_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 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (1024, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 1024), (1024, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1024, 4), (4, 1), torch.float32)
        buf7 = empty_strided_cuda((1024, 4), (4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_clamp_ge_le_logical_and_0[grid(4096)](primals_1,
            buf0, buf7, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(buf0, (4, 1024), (1, 4), 0), out=buf1)
        buf2 = reinterpret_tensor(buf0, (1024, 4), (1, 1024), 0)
        del buf0
        buf6 = empty_strided_cuda((4, 1024), (1024, 1), torch.bool)
        triton_poi_fused_clamp_ge_le_logical_and_0[grid(4096)](primals_3,
            buf2, buf6, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1),
            torch.float32)
        triton_poi_fused_gelu_1[grid(65536)](buf1, buf3, 65536, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 1024), (1024, 1), 0
            ), buf2, out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_gelu_2[grid(256)](buf4, buf5, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
    return buf5, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
        ), buf1, reinterpret_tensor(buf3, (64, 1024), (1024, 1), 0
        ), buf4, reinterpret_tensor(buf2, (4, 1024), (1024, 1), 0), buf6, buf7


class ReRegualizedLinearNACLayer(torch.nn.Module):

    def __init__(self, in_features, out_features, **kwargs):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.W = torch.nn.Parameter(torch.Tensor(out_features, in_features))
        self.register_parameter('bias', None)

    def reset_parameters(self):
        std = math.sqrt(2.0 / (self.in_features + self.out_features))
        r = min(0.5, math.sqrt(3.0) * std)
        torch.nn.init.uniform_(self.W, -r, r)

    def forward(self, input, reuse=False):
        W = torch.clamp(self.W, -1, 1)
        return torch.nn.functional.linear(input, W, self.bias)

    def extra_repr(self):
        return 'in_features={}, out_features={}'.format(self.in_features,
            self.out_features)


class ByteCombineNew(nn.Module):

    def __init__(self, input_dim, output_dim, inner_dim=1024, **kwags):
        super().__init__()
        self.layer_1 = ReRegualizedLinearNACLayer(input_dim, inner_dim)
        self.layer_2 = ReRegualizedLinearNACLayer(inner_dim, output_dim)
        self.act = nn.GELU()
        self.reset_parameters()

    def reset_parameters(self):
        self.layer_1.reset_parameters()
        self.layer_2.reset_parameters()

    def forward(self, input_0):
        primals_1 = self.layer_1.W
        primals_3 = self.layer_2.W
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
