# 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_7/inductor_cache/ep/ceps7alcqo6ixp7pux2qjaafeqqlxkyzwoeb2nun7wvzniy6qmmk.py
# Topologically Sorted Source Nodes: [x, mul, input_1], Original ATen: [aten.convolution, aten.mul, aten.sin]
# Source node to ATen node mapping:
#   input_1 => sin
#   mul => mul
#   x => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 30), kwargs = {})
#   %sin : [num_users=2] = call_function[target=torch.ops.aten.sin.default](args = (%mul,), kwargs = {})
triton_poi_fused_convolution_mul_sin_0 = async_compile.triton('triton_poi_fused_convolution_mul_sin_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: '*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_convolution_mul_sin_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_convolution_mul_sin_0(in_out_ptr0, in_ptr0, out_ptr0, 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)
    x3 = xindex
    x1 = (xindex // 16) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 30.0
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.sin(tmp4)
    tl.store(in_out_ptr0 + (x3), tmp2, None)
    tl.store(out_ptr0 + (x3), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/5t/c5t3onjsz7g4kgxerd23n3657yawwyguukgmxnhop4b73u3qinph.py
# Topologically Sorted Source Nodes: [x_1, mul_1, input_2], Original ATen: [aten.convolution, aten.mul, aten.sin]
# Source node to ATen node mapping:
#   input_2 => sin_1
#   mul_1 => mul_1
#   x_1 => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%sin, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, 30), kwargs = {})
#   %sin_1 : [num_users=2] = call_function[target=torch.ops.aten.sin.default](args = (%mul_1,), kwargs = {})
triton_poi_fused_convolution_mul_sin_1 = async_compile.triton('triton_poi_fused_convolution_mul_sin_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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 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_convolution_mul_sin_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_convolution_mul_sin_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 32
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 30.0
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.sin(tmp4)
    tl.store(in_out_ptr0 + (x3), tmp2, None)
    tl.store(out_ptr0 + (x3), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/l6/cl6hqobzfbynrauju3moqist73fdf3ff6zlakiewojjzu6bq24k6.py
# Topologically Sorted Source Nodes: [x_2, mul_2, input_3], Original ATen: [aten.convolution, aten.mul, aten.sin]
# Source node to ATen node mapping:
#   input_3 => sin_2
#   mul_2 => mul_2
#   x_2 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%sin_1, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_2, 30), kwargs = {})
#   %sin_2 : [num_users=1] = call_function[target=torch.ops.aten.sin.default](args = (%mul_2,), kwargs = {})
triton_poi_fused_convolution_mul_sin_2 = async_compile.triton('triton_poi_fused_convolution_mul_sin_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: '*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_convolution_mul_sin_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_convolution_mul_sin_2(in_out_ptr0, 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
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 30.0
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.sin(tmp4)
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp5, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args
    args.clear()
    assert_size_stride(primals_1, (64, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (32, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_5, (32, ), (1, ))
    assert_size_stride(primals_6, (4, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, mul, input_1], Original ATen: [aten.convolution, aten.mul, aten.sin]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_mul_sin_0.run(buf1, primals_2, buf2, 4096, grid=grid(4096), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 32, 4, 4), (512, 16, 4, 1))
        buf4 = buf3; del buf3  # reuse
        buf5 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1, mul_1, input_2], Original ATen: [aten.convolution, aten.mul, aten.sin]
        triton_poi_fused_convolution_mul_sin_1.run(buf4, primals_5, buf5, 2048, grid=grid(2048), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = buf6; del buf6  # reuse
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2, mul_2, input_3], Original ATen: [aten.convolution, aten.mul, aten.sin]
        triton_poi_fused_convolution_mul_sin_2.run(buf7, primals_7, buf8, 256, grid=grid(256), stream=stream0)
        del primals_7
    return (buf8, primals_1, primals_3, primals_4, primals_6, buf1, buf2, buf4, buf5, 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((64, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, 64, 1, 1), (64, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 32, 1, 1), (32, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    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 torch
import numpy as np
import torch.nn as nn


def act(act_fun='LeakyReLU'):
    """
        Either string defining an activation function or module (e.g. nn.ReLU)
    """
    if isinstance(act_fun, str):
        if act_fun == 'LeakyReLU':
            return nn.LeakyReLU(0.2, inplace=True)
        elif act_fun == 'Swish':
            return Swish()
        elif act_fun[:3] == 'ELU':
            if len(act_fun) > 3:
                param = float(act_fun[3:])
                return nn.ELU(param, inplace=True)
            return nn.ELU(inplace=True)
        elif act_fun == 'ReLU':
            return nn.ReLU()
        elif act_fun == 'tanh':
            return Tanh()
        elif act_fun == 'sine':
            return Sin()
        elif act_fun == 'soft':
            return nn.Softplus()
        elif act_fun == 'none':
            return nn.Sequential()
        else:
            assert False
    else:
        return act_fun()


class Swish(nn.Module):
    """
        https://arxiv.org/abs/1710.05941
        The hype was so huge that I could not help but try it
    """

    def __init__(self):
        super(Swish, self).__init__()
        self.s = nn.Sigmoid()

    def forward(self, x):
        return x * self.s(x)


class Tanh(nn.Module):
    """
        https://arxiv.org/abs/1710.05941
        The hype was so huge that I could not help but try it
    """

    def __init__(self):
        super(Tanh, self).__init__()

    def forward(self, x):
        return torch.tanh(x)


class Sin(nn.Module):
    """
        https://arxiv.org/abs/1710.05941
        The hype was so huge that I could not help but try it
    """

    def __init__(self):
        super(Sin, self).__init__()

    def forward(self, x):
        return torch.sin(x)


class SIREN_layer(nn.Module):

    def __init__(self, ch_in, ch_out, frist=False, act_fun='sine', omega_0=30):
        super(SIREN_layer, self).__init__()
        self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=1, bias
            =True)
        self.act_fun = act(act_fun)
        self.omega_0 = omega_0
        self.in_features = ch_in
        self.frist = frist
        self.init()

    def init(self):
        with torch.no_grad():
            if self.frist:
                self.conv1.weight.uniform_(-1 / self.in_features, 1 / self.
                    in_features)
            else:
                self.conv1.weight.uniform_(-np.sqrt(6 / self.in_features) /
                    self.omega_0, np.sqrt(6 / self.in_features) / self.omega_0)

    def forward(self, x):
        x = self.conv1(x)
        return self.act_fun(self.omega_0 * x)


class SIREN_CONV(nn.Module):

    def __init__(self, ch_in, ch_out):
        super(SIREN_CONV, self).__init__()
        self.conv1 = SIREN_layer(ch_in, 64, frist=True)
        self.conv2 = SIREN_layer(64, 32)
        self.conv3 = SIREN_layer(32, ch_out)
        self.conv = nn.Sequential(self.conv1, self.conv2, self.conv3)

    def forward(self, x):
        x = self.conv(x)
        return x


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


def get_init_inputs():
    return [[], {'ch_in': 4, 'ch_out': 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.triton_helpers import math as tl_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


@triton.jit
def triton_poi_fused_convolution_mul_sin_0(in_out_ptr0, 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)
    x3 = xindex
    x1 = xindex // 16 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 30.0
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.sin(tmp4)
    tl.store(in_out_ptr0 + x3, tmp2, None)
    tl.store(out_ptr0 + x3, tmp5, None)


@triton.jit
def triton_poi_fused_convolution_mul_sin_1(in_out_ptr0, 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)
    x3 = xindex
    x1 = xindex // 16 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 30.0
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.sin(tmp4)
    tl.store(in_out_ptr0 + x3, tmp2, None)
    tl.store(out_ptr0 + x3, tmp5, None)


@triton.jit
def triton_poi_fused_convolution_mul_sin_2(in_out_ptr0, 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
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 30.0
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.sin(tmp4)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp5, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (64, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (32, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (4, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_mul_sin_0[grid(4096)](buf1, primals_2,
            buf2, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 32, 4, 4), (512, 16, 4, 1))
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.float32
            )
        triton_poi_fused_convolution_mul_sin_1[grid(2048)](buf4, primals_5,
            buf5, 2048, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = buf6
        del buf6
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_convolution_mul_sin_2[grid(256)](buf7, primals_7,
            buf8, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
    return (buf8, primals_1, primals_3, primals_4, primals_6, buf1, buf2,
        buf4, buf5, buf7)


def act(act_fun='LeakyReLU'):
    """
        Either string defining an activation function or module (e.g. nn.ReLU)
    """
    if isinstance(act_fun, str):
        if act_fun == 'LeakyReLU':
            return nn.LeakyReLU(0.2, inplace=True)
        elif act_fun == 'Swish':
            return Swish()
        elif act_fun[:3] == 'ELU':
            if len(act_fun) > 3:
                param = float(act_fun[3:])
                return nn.ELU(param, inplace=True)
            return nn.ELU(inplace=True)
        elif act_fun == 'ReLU':
            return nn.ReLU()
        elif act_fun == 'tanh':
            return Tanh()
        elif act_fun == 'sine':
            return Sin()
        elif act_fun == 'soft':
            return nn.Softplus()
        elif act_fun == 'none':
            return nn.Sequential()
        else:
            assert False
    else:
        return act_fun()


class Swish(nn.Module):
    """
        https://arxiv.org/abs/1710.05941
        The hype was so huge that I could not help but try it
    """

    def __init__(self):
        super(Swish, self).__init__()
        self.s = nn.Sigmoid()

    def forward(self, x):
        return x * self.s(x)


class Tanh(nn.Module):
    """
        https://arxiv.org/abs/1710.05941
        The hype was so huge that I could not help but try it
    """

    def __init__(self):
        super(Tanh, self).__init__()

    def forward(self, x):
        return torch.tanh(x)


class Sin(nn.Module):
    """
        https://arxiv.org/abs/1710.05941
        The hype was so huge that I could not help but try it
    """

    def __init__(self):
        super(Sin, self).__init__()

    def forward(self, x):
        return torch.sin(x)


class SIREN_layer(nn.Module):

    def __init__(self, ch_in, ch_out, frist=False, act_fun='sine', omega_0=30):
        super(SIREN_layer, self).__init__()
        self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=1, bias
            =True)
        self.act_fun = act(act_fun)
        self.omega_0 = omega_0
        self.in_features = ch_in
        self.frist = frist
        self.init()

    def init(self):
        with torch.no_grad():
            if self.frist:
                self.conv1.weight.uniform_(-1 / self.in_features, 1 / self.
                    in_features)
            else:
                self.conv1.weight.uniform_(-np.sqrt(6 / self.in_features) /
                    self.omega_0, np.sqrt(6 / self.in_features) / self.omega_0)

    def forward(self, x):
        x = self.conv1(x)
        return self.act_fun(self.omega_0 * x)


class SIREN_CONVNew(nn.Module):

    def __init__(self, ch_in, ch_out):
        super(SIREN_CONVNew, self).__init__()
        self.conv1 = SIREN_layer(ch_in, 64, frist=True)
        self.conv2 = SIREN_layer(64, 32)
        self.conv3 = SIREN_layer(32, ch_out)
        self.conv = nn.Sequential(self.conv1, self.conv2, self.conv3)

    def forward(self, input_0):
        primals_1 = self.conv1.conv1.weight
        primals_2 = self.conv1.conv1.bias
        primals_4 = self.conv2.conv1.weight
        primals_5 = self.conv2.conv1.bias
        primals_6 = self.conv3.conv1.weight
        primals_7 = self.conv3.conv1.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
