# 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_4/inductor_cache/lj/cljvc2zpug6njnf62yjambxb7f4zf6o7zfec5edcglnqusgrpoks.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x => relu
# Graph fragment:
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le_3 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%view_6, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_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_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2000
    x1 = (xindex // 2000)
    tmp0 = tl.load(in_out_ptr0 + (x0 + (2016*x1)), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x0 + (2016*x1)), tmp4, xmask)
    tl.store(out_ptr0 + (x0 + (2048*x1)), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/rc/crca5szh4a5qf5qfgqrxrt7jnuuqhtxkxrcv3ak5x2g3d5ihmuue.py
# Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.view]
# Source node to ATen node mapping:
#   linear_1 => view_7
# Graph fragment:
#   %view_7 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%view_6, [64, 2000]), kwargs = {})
triton_poi_fused_view_1 = async_compile.triton('triton_poi_fused_view_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=[131072], 
    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_view_1', '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_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2000
    x1 = (xindex // 2000)
    tmp0 = tl.load(in_ptr0 + (x0 + (2016*x1) + (8064*((x1 % 4) // 4)) + (32256*(((4*((x1 // 4) % 4)) + (x1 % 4)) // 16))), xmask)
    tl.store(out_ptr0 + (x0 + (2016*x1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/u6/cu6ipqpgg6tkbtxvrv2anabfo4ic5vghymspicxhxr5xkjjjxmvd.py
# Topologically Sorted Source Nodes: [scale, shift], Original ATen: [aten.exp, aten.tanh]
# Source node to ATen node mapping:
#   scale => exp
#   shift => tanh
# Graph fragment:
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%slice_2,), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%slice_4,), kwargs = {})
triton_poi_fused_exp_tanh_2 = async_compile.triton('triton_poi_fused_exp_tanh_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: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_exp_tanh_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_exp_tanh_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 64)
    x3 = xindex % 64
    x0 = xindex % 8
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + (128*x2)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (64 + x3 + (128*x2)), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp4 + tmp1
    tmp6 = libdevice.tanh(tmp5)
    tl.store(out_ptr0 + (x4), tmp3, xmask)
    tl.store(out_ptr1 + (x4), tmp6, 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, primals_10, primals_11 = args
    args.clear()
    assert_size_stride(primals_1, (2000, 4), (4, 1))
    assert_size_stride(primals_2, (2000, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (2000, 2000), (2000, 1))
    assert_size_stride(primals_5, (2000, ), (1, ))
    assert_size_stride(primals_6, (2000, 2000), (2000, 1))
    assert_size_stride(primals_7, (2000, ), (1, ))
    assert_size_stride(primals_8, (2000, 2000), (2000, 1))
    assert_size_stride(primals_9, (2000, ), (1, ))
    assert_size_stride(primals_10, (8, 2000), (2000, 1))
    assert_size_stride(primals_11, (8, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 2000), (2016, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 2000), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 2000), (32256, 8064, 2016, 1), 0); del buf0  # reuse
        buf18 = empty_strided_cuda((4, 4, 4, 2000), (32768, 8192, 2048, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf18, 128000, grid=grid(128000), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((64, 2000), (2016, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.view]
        triton_poi_fused_view_1.run(buf1, buf2, 128000, grid=grid(128000), stream=stream0)
        buf3 = reinterpret_tensor(buf1, (64, 2000), (2016, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (2000, 2000), (1, 2000), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 2000), (32256, 8064, 2016, 1), 0); del buf3  # reuse
        buf17 = empty_strided_cuda((4, 4, 4, 2000), (32768, 8192, 2048, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf4, primals_5, buf17, 128000, grid=grid(128000), stream=stream0)
        del primals_5
        buf5 = empty_strided_cuda((64, 2000), (2016, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.view]
        triton_poi_fused_view_1.run(buf4, buf5, 128000, grid=grid(128000), stream=stream0)
        buf6 = reinterpret_tensor(buf4, (64, 2000), (2016, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf5, reinterpret_tensor(primals_6, (2000, 2000), (1, 2000), 0), out=buf6)
        buf7 = reinterpret_tensor(buf6, (4, 4, 4, 2000), (32256, 8064, 2016, 1), 0); del buf6  # reuse
        buf16 = empty_strided_cuda((4, 4, 4, 2000), (32768, 8192, 2048, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf7, primals_7, buf16, 128000, grid=grid(128000), stream=stream0)
        del primals_7
        buf8 = empty_strided_cuda((64, 2000), (2016, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.view]
        triton_poi_fused_view_1.run(buf7, buf8, 128000, grid=grid(128000), stream=stream0)
        buf9 = reinterpret_tensor(buf7, (64, 2000), (2016, 1), 0); del buf7  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf8, reinterpret_tensor(primals_8, (2000, 2000), (1, 2000), 0), out=buf9)
        buf10 = reinterpret_tensor(buf9, (4, 4, 4, 2000), (32256, 8064, 2016, 1), 0); del buf9  # reuse
        buf15 = empty_strided_cuda((4, 4, 4, 2000), (32768, 8192, 2048, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf10, primals_9, buf15, 128000, grid=grid(128000), stream=stream0)
        del primals_9
        buf11 = empty_strided_cuda((64, 2000), (2016, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.view]
        triton_poi_fused_view_1.run(buf10, buf11, 128000, grid=grid(128000), stream=stream0)
        del buf10
        buf12 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf11, reinterpret_tensor(primals_10, (2000, 8), (1, 2000), 0), out=buf12)
        buf13 = empty_strided_cuda((4, 2, 4, 8), (64, 32, 8, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 2, 4, 8), (64, 32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [scale, shift], Original ATen: [aten.exp, aten.tanh]
        triton_poi_fused_exp_tanh_2.run(buf12, primals_11, buf13, buf14, 256, grid=grid(256), stream=stream0)
        del buf12
        del primals_11
    return (buf13, buf14, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf2, buf5, buf8, buf11, buf13, buf14, primals_10, buf15, primals_8, buf16, primals_6, buf17, primals_4, buf18, )


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((2000, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((2000, ), (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((2000, 2000), (2000, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((2000, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((2000, 2000), (2000, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((2000, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((2000, 2000), (2000, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((2000, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((8, 2000), (2000, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((8, ), (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, primals_10, primals_11])
    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.nn as nn


class ElemAffineNetwork(nn.Module):
    """Network for parameterizing affine transformation"""

    def __init__(self, input_dim):
        super(ElemAffineNetwork, self).__init__()
        self.input_dim = input_dim
        self.fc1 = nn.Linear(input_dim, 2000)
        self.relu1 = nn.ReLU(inplace=True)
        self.fc2 = nn.Linear(2000, 2000)
        self.relu2 = nn.ReLU(inplace=True)
        self.fc3 = nn.Linear(2000, 2000)
        self.relu3 = nn.ReLU(inplace=True)
        self.fc4 = nn.Linear(2000, 2000)
        self.relu4 = nn.ReLU(inplace=True)
        self.fc5 = nn.Linear(2000, 2 * input_dim)
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.constant_(m.weight, 0)

    def forward(self, x):
        x = self.relu1(self.fc1(x))
        x = self.relu2(self.fc2(x))
        x = self.relu3(self.fc3(x))
        x = self.relu4(self.fc4(x))
        x = self.fc5(x)
        scale = torch.exp(x[:, :self.input_dim // 2])
        shift = torch.tanh(x[:, self.input_dim // 2:])
        return scale, shift


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


def get_init_inputs():
    return [[], {'input_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, math as tl_math
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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2000
    x1 = xindex // 2000
    tmp0 = tl.load(in_out_ptr0 + (x0 + 2016 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x0 + 2016 * x1), tmp4, xmask)
    tl.store(out_ptr0 + (x0 + 2048 * x1), tmp6, xmask)


@triton.jit
def triton_poi_fused_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2000
    x1 = xindex // 2000
    tmp0 = tl.load(in_ptr0 + (x0 + 2016 * x1 + 8064 * (x1 % 4 // 4) + 32256 *
        ((4 * (x1 // 4 % 4) + x1 % 4) // 16)), xmask)
    tl.store(out_ptr0 + (x0 + 2016 * x1), tmp0, xmask)


@triton.jit
def triton_poi_fused_exp_tanh_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 64
    x3 = xindex % 64
    x0 = xindex % 8
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 128 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (64 + x3 + 128 * x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp4 + tmp1
    tmp6 = libdevice.tanh(tmp5)
    tl.store(out_ptr0 + x4, tmp3, xmask)
    tl.store(out_ptr1 + x4, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (2000, 4), (4, 1))
    assert_size_stride(primals_2, (2000,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (2000, 2000), (2000, 1))
    assert_size_stride(primals_5, (2000,), (1,))
    assert_size_stride(primals_6, (2000, 2000), (2000, 1))
    assert_size_stride(primals_7, (2000,), (1,))
    assert_size_stride(primals_8, (2000, 2000), (2000, 1))
    assert_size_stride(primals_9, (2000,), (1,))
    assert_size_stride(primals_10, (8, 2000), (2000, 1))
    assert_size_stride(primals_11, (8,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 2000), (2016, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 2000), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 2000), (32256, 8064, 2016,
            1), 0)
        del buf0
        buf18 = empty_strided_cuda((4, 4, 4, 2000), (32768, 8192, 2048, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(128000)](buf1,
            primals_2, buf18, 128000, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 2000), (2016, 1), torch.float32)
        triton_poi_fused_view_1[grid(128000)](buf1, buf2, 128000, XBLOCK=
            1024, num_warps=4, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (64, 2000), (2016, 1), 0)
        del buf1
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (2000, 2000),
            (1, 2000), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 2000), (32256, 8064, 2016,
            1), 0)
        del buf3
        buf17 = empty_strided_cuda((4, 4, 4, 2000), (32768, 8192, 2048, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(128000)](buf4,
            primals_5, buf17, 128000, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((64, 2000), (2016, 1), torch.float32)
        triton_poi_fused_view_1[grid(128000)](buf4, buf5, 128000, XBLOCK=
            1024, num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (64, 2000), (2016, 1), 0)
        del buf4
        extern_kernels.mm(buf5, reinterpret_tensor(primals_6, (2000, 2000),
            (1, 2000), 0), out=buf6)
        buf7 = reinterpret_tensor(buf6, (4, 4, 4, 2000), (32256, 8064, 2016,
            1), 0)
        del buf6
        buf16 = empty_strided_cuda((4, 4, 4, 2000), (32768, 8192, 2048, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(128000)](buf7,
            primals_7, buf16, 128000, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_7
        buf8 = empty_strided_cuda((64, 2000), (2016, 1), torch.float32)
        triton_poi_fused_view_1[grid(128000)](buf7, buf8, 128000, XBLOCK=
            1024, num_warps=4, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (64, 2000), (2016, 1), 0)
        del buf7
        extern_kernels.mm(buf8, reinterpret_tensor(primals_8, (2000, 2000),
            (1, 2000), 0), out=buf9)
        buf10 = reinterpret_tensor(buf9, (4, 4, 4, 2000), (32256, 8064, 
            2016, 1), 0)
        del buf9
        buf15 = empty_strided_cuda((4, 4, 4, 2000), (32768, 8192, 2048, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(128000)](buf10,
            primals_9, buf15, 128000, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_9
        buf11 = empty_strided_cuda((64, 2000), (2016, 1), torch.float32)
        triton_poi_fused_view_1[grid(128000)](buf10, buf11, 128000, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del buf10
        buf12 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.mm(buf11, reinterpret_tensor(primals_10, (2000, 8),
            (1, 2000), 0), out=buf12)
        buf13 = empty_strided_cuda((4, 2, 4, 8), (64, 32, 8, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 2, 4, 8), (64, 32, 8, 1), torch.float32)
        triton_poi_fused_exp_tanh_2[grid(256)](buf12, primals_11, buf13,
            buf14, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf12
        del primals_11
    return (buf13, buf14, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
        buf2, buf5, buf8, buf11, buf13, buf14, primals_10, buf15, primals_8,
        buf16, primals_6, buf17, primals_4, buf18)


class ElemAffineNetworkNew(nn.Module):
    """Network for parameterizing affine transformation"""

    def __init__(self, input_dim):
        super(ElemAffineNetworkNew, self).__init__()
        self.input_dim = input_dim
        self.fc1 = nn.Linear(input_dim, 2000)
        self.relu1 = nn.ReLU(inplace=True)
        self.fc2 = nn.Linear(2000, 2000)
        self.relu2 = nn.ReLU(inplace=True)
        self.fc3 = nn.Linear(2000, 2000)
        self.relu3 = nn.ReLU(inplace=True)
        self.fc4 = nn.Linear(2000, 2000)
        self.relu4 = nn.ReLU(inplace=True)
        self.fc5 = nn.Linear(2000, 2 * input_dim)
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.constant_(m.weight, 0)

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_8 = self.fc4.weight
        primals_9 = self.fc4.bias
        primals_10 = self.fc5.weight
        primals_11 = self.fc5.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0], output[1]
