from torch.nn import Module
import torch
from torch import Tensor
from torch.nn import Identity
from torch.nn.modules import Module
import torch.optim.lr_scheduler


class AbsLayer(Module):

    def forward(self, x: 'Tensor') ->Tensor:
        return torch.abs(x).reshape((-1, 1))


class AbsModel(Module):
    """Fake model, that simply compute the absolute value of the inputs"""

    def __init__(self):
        super().__init__()
        self.features = AbsLayer()
        self.classifier = Identity()

    def forward(self, x: 'Tensor') ->Tensor:
        x = self.features(x)
        x = self.classifier(x)
        return x


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


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

import torch
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
from torch.nn import Module
from torch import Tensor
from torch.nn import Identity
from torch.nn.modules import Module
import torch.optim.lr_scheduler
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_abs_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl_math.abs(tmp0)
    tl.store(out_ptr0 + x0, tmp1, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 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)
        get_raw_stream(0)
        triton_poi_fused_abs_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return reinterpret_tensor(buf0, (256, 1), (1, 1), 0),


class AbsLayer(Module):

    def forward(self, x: 'Tensor') ->Tensor:
        return torch.abs(x).reshape((-1, 1))


class AbsModelNew(Module):
    """Fake model, that simply compute the absolute value of the inputs"""

    def __init__(self):
        super().__init__()
        self.features = AbsLayer()
        self.classifier = Identity()

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
