# 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/tr/ctr4zp7r7yiztjwikrn4nrv7z7rsejvoxfqymoocauxbdydbltl7.py
# Topologically Sorted Source Nodes: [inp], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   inp => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%view, %primals_2], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = (xindex // 2)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x1), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/tm/ctmrojiqvcjkqmyqyqksmtxds456nlkyuhqryquqf76hvpwtsd6z.py
# Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   input_2 => gt, mul, where
# Graph fragment:
#   %add_tensor_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_2, %primals_4), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_tensor_2, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_tensor_2, 0.2), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %add_tensor_2, %mul), kwargs = {})
triton_poi_fused_leaky_relu_1 = async_compile.triton('triton_poi_fused_leaky_relu_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=[128], 
    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_leaky_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_leaky_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/xh/cxhwbkficswmnt3c2blivsmnyhqi55whtbohiqyxy2qak7jn4aee.py
# Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   input_6 => gt_2, mul_2, where_2
# Graph fragment:
#   %add_tensor : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_8), kwargs = {})
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_tensor, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_tensor, 0.2), kwargs = {})
#   %where_2 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %add_tensor, %mul_2), kwargs = {})
triton_poi_fused_leaky_relu_2 = async_compile.triton('triton_poi_fused_leaky_relu_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=[64], 
    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_leaky_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_leaky_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + (x2), tmp7, 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, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1), (1, 1))
    assert_size_stride(primals_2, (4, 1), (1, 1))
    assert_size_stride(primals_3, (32, 2), (2, 1))
    assert_size_stride(primals_4, (32, ), (1, ))
    assert_size_stride(primals_5, (32, 32), (32, 1))
    assert_size_stride(primals_6, (32, ), (1, ))
    assert_size_stride(primals_7, (16, 32), (32, 1))
    assert_size_stride(primals_8, (16, ), (1, ))
    assert_size_stride(primals_9, (1, 16), (16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [inp], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 8, grid=grid(8), stream=stream0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (2, 32), (1, 2), 0), out=buf1)
        del primals_3
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_1.run(buf2, primals_4, 128, grid=grid(128), stream=stream0)
        del primals_4
        buf3 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (32, 32), (1, 32), 0), out=buf3)
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_1.run(buf4, primals_6, 128, grid=grid(128), stream=stream0)
        del primals_6
        buf5 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf4, reinterpret_tensor(primals_7, (32, 16), (1, 32), 0), out=buf5)
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_2.run(buf6, primals_8, 64, grid=grid(64), stream=stream0)
        del primals_8
        buf7 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.mm]
        extern_kernels.mm(buf6, reinterpret_tensor(primals_9, (16, 1), (1, 16), 0), out=buf7)
    return (buf7, buf0, buf2, buf4, buf6, primals_9, primals_7, primals_5, )


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, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((32, 2), (2, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((16, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((1, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    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 torch.utils.data
import torch.nn as nn


def add_layer(seq, ix, n_inputs, n_outputs, nonlin, normalization):
    seq.add_module('L' + str(ix), nn.Linear(n_inputs, n_outputs))
    if ix > 0 and normalization:
        if normalization == 'LN':
            seq.main.add_module('A' + str(ix), nn.LayerNorm(n_outputs))
        else:
            raise ValueError('Unknown normalization: {}'.format(normalization))
    if nonlin == 'LeakyReLU':
        seq.add_module('N' + str(ix), nn.LeakyReLU(0.2, inplace=True))
    elif nonlin == 'ReLU':
        seq.add_module('N' + str(ix), nn.ReLU(inplace=True))
    elif nonlin == 'Sigmoid':
        seq.add_module('N' + str(ix), nn.Sigmoid())


class D_concat(nn.Module):

    def __init__(self, insizes=[1, 1], layerSizes=[32, 32, 16], nonlin=
        'LeakyReLU', normalization=None):
        super(D_concat, self).__init__()
        insize = sum(insizes)
        self.main = nn.Sequential()
        for ix, n_inputs, n_outputs in zip(range(len(layerSizes)), [insize] +
            layerSizes[:-1], layerSizes):
            add_layer(self.main, ix, n_inputs, n_outputs, nonlin, normalization
                )
            self.PhiD = n_outputs
        self.V = nn.Linear(self.PhiD, 1, bias=False)
        self.V.weight.data *= 100

    def forward(self, x, y):
        x = x.view(x.size(0), -1)
        y = y.view(x.size(0), 1)
        inp = torch.cat([x, y], dim=1)
        phi = self.main(inp)
        return self.V(phi)


def get_inputs():
    return [torch.rand([4, 1]), torch.rand([4, 1])]


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

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
import torch.utils.data
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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp9 = tl.load(in_ptr1 + x1, tmp6 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x2, tmp7, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1), (1, 1))
    assert_size_stride(primals_2, (4, 1), (1, 1))
    assert_size_stride(primals_3, (32, 2), (2, 1))
    assert_size_stride(primals_4, (32,), (1,))
    assert_size_stride(primals_5, (32, 32), (32, 1))
    assert_size_stride(primals_6, (32,), (1,))
    assert_size_stride(primals_7, (16, 32), (32, 1))
    assert_size_stride(primals_8, (16,), (1,))
    assert_size_stride(primals_9, (1, 16), (16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(8)](primals_1, primals_2, buf0, 8,
            XBLOCK=8, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (2, 32), (1, 
            2), 0), out=buf1)
        del primals_3
        buf2 = buf1
        del buf1
        triton_poi_fused_leaky_relu_1[grid(128)](buf2, primals_4, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (32, 32), (1,
            32), 0), out=buf3)
        buf4 = buf3
        del buf3
        triton_poi_fused_leaky_relu_1[grid(128)](buf4, primals_6, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_6
        buf5 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(buf4, reinterpret_tensor(primals_7, (32, 16), (1,
            32), 0), out=buf5)
        buf6 = buf5
        del buf5
        triton_poi_fused_leaky_relu_2[grid(64)](buf6, primals_8, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del primals_8
        buf7 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf6, reinterpret_tensor(primals_9, (16, 1), (1, 
            16), 0), out=buf7)
    return buf7, buf0, buf2, buf4, buf6, primals_9, primals_7, primals_5


def add_layer(seq, ix, n_inputs, n_outputs, nonlin, normalization):
    seq.add_module('L' + str(ix), nn.Linear(n_inputs, n_outputs))
    if ix > 0 and normalization:
        if normalization == 'LN':
            seq.main.add_module('A' + str(ix), nn.LayerNorm(n_outputs))
        else:
            raise ValueError('Unknown normalization: {}'.format(normalization))
    if nonlin == 'LeakyReLU':
        seq.add_module('N' + str(ix), nn.LeakyReLU(0.2, inplace=True))
    elif nonlin == 'ReLU':
        seq.add_module('N' + str(ix), nn.ReLU(inplace=True))
    elif nonlin == 'Sigmoid':
        seq.add_module('N' + str(ix), nn.Sigmoid())


class D_concatNew(nn.Module):

    def __init__(self, insizes=[1, 1], layerSizes=[32, 32, 16], nonlin=
        'LeakyReLU', normalization=None):
        super(D_concatNew, self).__init__()
        insize = sum(insizes)
        self.main = nn.Sequential()
        for ix, n_inputs, n_outputs in zip(range(len(layerSizes)), [insize] +
            layerSizes[:-1], layerSizes):
            add_layer(self.main, ix, n_inputs, n_outputs, nonlin, normalization
                )
            self.PhiD = n_outputs
        self.V = nn.Linear(self.PhiD, 1, bias=False)
        self.V.weight.data *= 100

    def forward(self, input_0, input_1):
        primals_3 = self.main.L0.weight
        primals_4 = self.main.L0.bias
        primals_5 = self.main.L1.weight
        primals_6 = self.main.L1.bias
        primals_7 = self.main.L2.weight
        primals_8 = self.main.L2.bias
        primals_9 = self.V.weight
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
