# 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/2h/c2hyujbqipl7k6j47bijuxarwsprhgffgwjyb3pnhunnolvethht.py
# Topologically Sorted Source Nodes: [residual, tensor, one, eq, tensor_1], Original ATen: [aten.clone, aten.sign, aten.ones_like, aten.eq, aten.where]
# Source node to ATen node mapping:
#   eq => eq
#   one => full_default
#   residual => clone
#   tensor => sign
#   tensor_1 => where
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%primals_1,), kwargs = {})
#   %sign : [num_users=2] = call_function[target=torch.ops.aten.sign.default](args = (%primals_1,), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4, 4], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%sign, 0), kwargs = {})
#   %where : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %sign), kwargs = {})
triton_poi_fused_clone_eq_ones_like_sign_where_0 = async_compile.triton('triton_poi_fused_clone_eq_ones_like_sign_where_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=[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_clone_eq_ones_like_sign_where_0', '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_clone_eq_ones_like_sign_where_0(in_ptr0, 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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp1 < tmp0
    tmp3 = tmp2.to(tl.int8)
    tmp4 = tmp0 < tmp1
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp3 - tmp5
    tmp7 = tmp6.to(tmp0.dtype)
    tmp8 = 0.0
    tmp9 = tmp7 == tmp8
    tmp10 = 1.0
    tmp11 = tl.where(tmp9, tmp10, tmp7)
    tl.store(out_ptr0 + (x0), tmp0, xmask)
    tl.store(out_ptr1 + (x0), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ru/crueu4jwh7dhznjh46ggqi5ktaws4vzu6wmquhabrpdknhgumwzh.py
# Topologically Sorted Source Nodes: [tensor_2, one_1, eq_1, tensor_3], Original ATen: [aten.sign, aten.ones_like, aten.eq, aten.where]
# Source node to ATen node mapping:
#   eq_1 => eq_1
#   one_1 => full_default_1
#   tensor_2 => sign_1
#   tensor_3 => where_1
# Graph fragment:
#   %sign_1 : [num_users=2] = call_function[target=torch.ops.aten.sign.default](args = (%primals_3,), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 3, 3], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %eq_1 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%sign_1, 0), kwargs = {})
#   %where_1 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%eq_1, %full_default_1, %sign_1), kwargs = {})
triton_poi_fused_eq_ones_like_sign_where_1 = async_compile.triton('triton_poi_fused_eq_ones_like_sign_where_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=[256], 
    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_eq_ones_like_sign_where_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_eq_ones_like_sign_where_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 144
    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 = tl.full([1], 0, tl.int32)
    tmp2 = tmp1 < tmp0
    tmp3 = tmp2.to(tl.int8)
    tmp4 = tmp0 < tmp1
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp3 - tmp5
    tmp7 = tmp6.to(tmp0.dtype)
    tmp8 = 0.0
    tmp9 = tmp7 == tmp8
    tmp10 = 1.0
    tmp11 = tl.where(tmp9, tmp10, tmp7)
    tl.store(out_ptr0 + (x0), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/td/ctdf2ohamonauwhkpjgiz662xpkp5ahv4sfrwu2ixlvyc4uewpxq.py
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten._native_batch_norm_legit_no_training, aten.hardtanh]
# Source node to ATen node mapping:
#   out_1 => add_1, mul_1, mul_2, sub_2
#   out_2 => clamp_max, clamp_min
# Graph fragment:
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution, %unsqueeze_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %unsqueeze_3), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %unsqueeze_5), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %unsqueeze_7), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_1, -1.0), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1.0), kwargs = {})
triton_poi_fused__native_batch_norm_legit_no_training_hardtanh_2 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_no_training_hardtanh_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: '*fp32', 5: '*fp32', 6: '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, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_no_training_hardtanh_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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__native_batch_norm_legit_no_training_hardtanh_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1e-05
    tmp5 = tmp3 + tmp4
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = tl.full([1], 1, tl.int32)
    tmp8 = tmp7 / tmp6
    tmp9 = 1.0
    tmp10 = tmp8 * tmp9
    tmp11 = tmp2 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = -1.0
    tmp17 = triton_helpers.maximum(tmp15, tmp16)
    tmp18 = triton_helpers.minimum(tmp17, tmp9)
    tl.store(out_ptr0 + (x3), tmp18, 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, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [residual, tensor, one, eq, tensor_1], Original ATen: [aten.clone, aten.sign, aten.ones_like, aten.eq, aten.where]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_eq_ones_like_sign_where_0.run(primals_1, buf0, buf1, 256, grid=grid(256), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tensor_2, one_1, eq_1, tensor_3], Original ATen: [aten.sign, aten.ones_like, aten.eq, aten.where]
        triton_poi_fused_eq_ones_like_sign_where_1.run(primals_3, buf2, 144, grid=grid(144), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf1, buf2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten._native_batch_norm_legit_no_training, aten.hardtanh]
        triton_poi_fused__native_batch_norm_legit_no_training_hardtanh_2.run(buf3, primals_4, primals_5, primals_6, primals_7, buf4, 256, grid=grid(256), stream=stream0)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        buf5 = torch.ops.aten.set_.source_Tensor(primals_1, buf1)
        assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1))
        del primals_1
        # Topologically Sorted Source Nodes: [], Original ATen: []
        buf10 = torch.ops.aten.set_.source_Tensor(primals_2, buf2)
        assert_size_stride(buf10, (4, 4, 3, 3), (36, 9, 3, 1))
        del primals_2
    return (buf4, buf0, primals_4, primals_5, primals_6, primals_7, buf1, buf2, buf3, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (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 torch.utils.data
from torch import nn
import torch
import torch.nn.parallel
import torch.optim


def Binarize(tensor, quant_mode='det'):
    if quant_mode == 'det':
        tensor = tensor.sign()
        zero = torch.zeros_like(tensor)
        one = torch.ones_like(tensor)
        zero - one
        tensor = torch.where(tensor == 0, one, tensor)
        return tensor
    else:
        return tensor.add_(1).div_(2).add_(torch.rand(tensor.size()).add(-0.5)
            ).clamp_(0, 1).round().mul_(2).add_(-1)


def Binaryconv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return BinarizeConv2d(in_planes, out_planes, kernel_size=3, stride=
        stride, padding=1, bias=False)


class BinarizeConv2d(nn.Conv2d):

    def __init__(self, *kargs, **kwargs):
        super(BinarizeConv2d, self).__init__(*kargs, **kwargs)

    def forward(self, input):
        input.data = Binarize(input.data)
        if not hasattr(self.weight, 'org'):
            self.weight.org = self.weight.data.clone()
        self.weight.data = Binarize(self.weight.org)
        out = nn.functional.conv2d(input, self.weight, None, self.stride,
            self.padding, self.dilation, self.groups)
        if self.bias is not None:
            self.bias.org = self.bias.data.clone()
            out += self.bias.view(1, -1, 1, 1).expand_as(out)
        return out


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None,
        do_bntan=True):
        super(BasicBlock, self).__init__()
        self.conv1 = Binaryconv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.tanh1 = nn.Hardtanh(inplace=True)
        self.conv2 = Binaryconv3x3(planes, planes)
        self.tanh2 = nn.Hardtanh(inplace=True)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.do_bntan = do_bntan
        self.stride = stride

    def forward(self, x):
        residual = x.clone()
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.tanh1(out)
        out = self.conv2(out)
        if self.downsample is not None:
            if residual.data.max() > 1:
                pdb.set_trace()
            residual = self.downsample(residual)
        out += residual
        if self.do_bntan:
            out = self.bn2(out)
            out = self.tanh2(out)
        return out


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


def get_init_inputs():
    return [[], {'inplanes': 4, 'planes': 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 torch.utils.data
from torch import nn
import torch
import torch.nn.parallel
import torch.optim
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_clone_eq_ones_like_sign_where_0(in_ptr0, 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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp1 < tmp0
    tmp3 = tmp2.to(tl.int8)
    tmp4 = tmp0 < tmp1
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp3 - tmp5
    tmp7 = tmp6.to(tmp0.dtype)
    tmp8 = 0.0
    tmp9 = tmp7 == tmp8
    tmp10 = 1.0
    tmp11 = tl.where(tmp9, tmp10, tmp7)
    tl.store(out_ptr0 + x0, tmp0, xmask)
    tl.store(out_ptr1 + x0, tmp11, xmask)


@triton.jit
def triton_poi_fused_eq_ones_like_sign_where_1(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 144
    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 = tl.full([1], 0, tl.int32)
    tmp2 = tmp1 < tmp0
    tmp3 = tmp2.to(tl.int8)
    tmp4 = tmp0 < tmp1
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp3 - tmp5
    tmp7 = tmp6.to(tmp0.dtype)
    tmp8 = 0.0
    tmp9 = tmp7 == tmp8
    tmp10 = 1.0
    tmp11 = tl.where(tmp9, tmp10, tmp7)
    tl.store(out_ptr0 + x0, tmp11, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_no_training_hardtanh_2(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1e-05
    tmp5 = tmp3 + tmp4
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = tl.full([1], 1, tl.int32)
    tmp8 = tmp7 / tmp6
    tmp9 = 1.0
    tmp10 = tmp8 * tmp9
    tmp11 = tmp2 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = -1.0
    tmp17 = triton_helpers.maximum(tmp15, tmp16)
    tmp18 = triton_helpers.minimum(tmp17, tmp9)
    tl.store(out_ptr0 + x3, tmp18, 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, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_eq_ones_like_sign_where_0[grid(256)](primals_1,
            buf0, buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        triton_poi_fused_eq_ones_like_sign_where_1[grid(144)](primals_3,
            buf2, 144, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf3 = extern_kernels.convolution(buf1, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__native_batch_norm_legit_no_training_hardtanh_2[grid
            (256)](buf3, primals_4, primals_5, primals_6, primals_7, buf4, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = torch.ops.aten.set_.source_Tensor(primals_1, buf1)
        assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1))
        del primals_1
        buf10 = torch.ops.aten.set_.source_Tensor(primals_2, buf2)
        assert_size_stride(buf10, (4, 4, 3, 3), (36, 9, 3, 1))
        del primals_2
    return (buf4, buf0, primals_4, primals_5, primals_6, primals_7, buf1,
        buf2, buf3)


def Binarize(tensor, quant_mode='det'):
    if quant_mode == 'det':
        tensor = tensor.sign()
        zero = torch.zeros_like(tensor)
        one = torch.ones_like(tensor)
        zero - one
        tensor = torch.where(tensor == 0, one, tensor)
        return tensor
    else:
        return tensor.add_(1).div_(2).add_(torch.rand(tensor.size()).add(-0.5)
            ).clamp_(0, 1).round().mul_(2).add_(-1)


def Binaryconv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return BinarizeConv2d(in_planes, out_planes, kernel_size=3, stride=
        stride, padding=1, bias=False)


class BinarizeConv2d(nn.Conv2d):

    def __init__(self, *kargs, **kwargs):
        super(BinarizeConv2d, self).__init__(*kargs, **kwargs)

    def forward(self, input):
        input.data = Binarize(input.data)
        if not hasattr(self.weight, 'org'):
            self.weight.org = self.weight.data.clone()
        self.weight.data = Binarize(self.weight.org)
        out = nn.functional.conv2d(input, self.weight, None, self.stride,
            self.padding, self.dilation, self.groups)
        if self.bias is not None:
            self.bias.org = self.bias.data.clone()
            out += self.bias.view(1, -1, 1, 1).expand_as(out)
        return out


class BasicBlockNew(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None,
        do_bntan=True):
        super(BasicBlockNew, self).__init__()
        self.conv1 = Binaryconv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.tanh1 = nn.Hardtanh(inplace=True)
        self.conv2 = Binaryconv3x3(planes, planes)
        self.tanh2 = nn.Hardtanh(inplace=True)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.do_bntan = do_bntan
        self.stride = stride

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_4 = self.bn1.weight
        primals_5 = self.bn1.bias
        primals_3 = self.conv2.weight
        primals_6 = self.bn2.weight
        primals_7 = self.bn2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
