# AOT ID: ['0_inference']
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/nc/cncq7jlbf7req5ht7vs4lajl6zdv5jinugrmeved2y7rieiv63cg.py
# Topologically Sorted Source Nodes: [setitem], Original ATen: [aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   setitem => full_default, index_put
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 2.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put : [num_users=2] = call_function[target=torch.ops.aten.index_put_.default](args = (%arg1_1, [%ge], %full_default), kwargs = {})
triton_poi_fused_index_put_lift_fresh_0 = async_compile.triton('triton_poi_fused_index_put_lift_fresh_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: '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_index_put_lift_fresh_0', 'mutated_arg_names': ['in_ptr0', 'out_ptr0'], 'no_x_dim': False, 'num_load': 1, '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_index_put_lift_fresh_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 = 0.9
    tmp2 = tmp0 >= tmp1
    tmp3 = 2.0
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/kn/ckndrvngdbo3rrepy6nu5g7xshvxtbdxbgemeoicukp54reaos4y.py
# Topologically Sorted Source Nodes: [setitem_1], Original ATen: [aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   setitem_1 => full_default_1, index_put_1
# Graph fragment:
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_1 : [num_users=2] = call_function[target=torch.ops.aten.index_put_.default](args = (%index_put, [%lt], %full_default_1), kwargs = {})
triton_poi_fused_index_put_lift_fresh_1 = async_compile.triton('triton_poi_fused_index_put_lift_fresh_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=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_index_put_lift_fresh_1', 'mutated_arg_names': ['in_ptr0', 'out_ptr0'], 'no_x_dim': False, 'num_load': 1, '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_index_put_lift_fresh_1(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 = 0.9
    tmp2 = tmp0 < tmp1
    tmp3 = 1.0
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ho/chom2bhiwf5qu3yh2stu4bihs3qshknsqtsoqy6xmdfjqaf4a3yc.py
# Topologically Sorted Source Nodes: [setitem_2, sum_1], Original ATen: [aten.lift_fresh, aten.index_put, aten.sum]
# Source node to ATen node mapping:
#   setitem_2 => full_default_2, index_put_2
#   sum_1 => sum_1
# Graph fragment:
#   %full_default_2 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_2 : [num_users=3] = call_function[target=torch.ops.aten.index_put_.default](args = (%index_put_1, [%eq], %full_default_2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%index_put_2,), kwargs = {})
triton_per_fused_index_put_lift_fresh_sum_2 = async_compile.triton('triton_per_fused_index_put_lift_fresh_sum_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.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_index_put_lift_fresh_sum_2', 'mutated_arg_names': ['in_ptr0', 'out_ptr0'], 'no_x_dim': True, 'num_load': 1, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_index_put_lift_fresh_sum_2(in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = 2.0
    tmp2 = tmp0 == tmp1
    tmp3 = 0.0
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tl.store(out_ptr0 + (tl.broadcast_to(r0, [RBLOCK])), tmp4, None)
    tl.store(out_ptr1 + (tl.full([1], 0, tl.int32)), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/az/caznebbkxbo5xyds3w5qgcukajrizzf3vwnjnwn6lxjvefgg4d3u.py
# Topologically Sorted Source Nodes: [mul, mul_1, truediv], Original ATen: [aten.mul, aten.div]
# Source node to ATen node mapping:
#   mul => mul
#   mul_1 => mul_1
#   truediv => div
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %index_put_2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, 256), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %sum_1), kwargs = {})
triton_poi_fused_div_mul_3 = async_compile.triton('triton_poi_fused_div_mul_3', '''
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, 4], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_mul_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_div_mul_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 16
    y1 = (yindex // 16)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + ((4*x2) + (16*(y0 // 4)) + (64*y1) + (y0 % 4)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2 + (4*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, YBLOCK])
    tmp2 = tmp0 * tmp1
    tmp3 = 256.0
    tmp4 = tmp2 * tmp3
    tmp7 = tmp4 / tmp6
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp7, xmask & ymask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 16, 4), (64, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [setitem], Original ATen: [aten.lift_fresh, aten.index_put]
        stream0 = get_raw_stream(0)
        triton_poi_fused_index_put_lift_fresh_0.run(arg1_1, arg1_1, 256, grid=grid(256), stream=stream0)
        # Topologically Sorted Source Nodes: [setitem_1], Original ATen: [aten.lift_fresh, aten.index_put]
        triton_poi_fused_index_put_lift_fresh_1.run(arg1_1, arg1_1, 256, grid=grid(256), stream=stream0)
        buf6 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [setitem_2, sum_1], Original ATen: [aten.lift_fresh, aten.index_put, aten.sum]
        triton_per_fused_index_put_lift_fresh_sum_2.run(arg1_1, arg1_1, buf6, 1, 256, grid=grid(1), stream=stream0)
        buf7 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, mul_1, truediv], Original ATen: [aten.mul, aten.div]
        triton_poi_fused_div_mul_3.run(arg0_1, arg1_1, buf6, buf7, 64, 4, grid=grid(64, 4), stream=stream0)
        del arg0_1
        del arg1_1
        del buf6
    return (reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 1, 4), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 16, 4), (64, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    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 DropBlockT_1d(nn.Module):

    def __init__(self, keep_prob=0.9):
        super(DropBlockT_1d, self).__init__()
        self.keep_prob = keep_prob

    def forward(self, input, mask):
        n, c, t, v = input.size()
        input1 = input.permute(0, 1, 3, 2).contiguous().view(n, c * v, t)
        mask[mask >= self.keep_prob] = 2.0
        mask[mask < self.keep_prob] = 1.0
        mask[mask == 2.0] = 0.0
        return (input1 * mask * mask.numel() / mask.sum()).view(n, c, v, t
            ).permute(0, 1, 3, 2)


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 16, 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 import triton_helpers
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_index_put_lift_fresh_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 = 0.9
    tmp2 = tmp0 >= tmp1
    tmp3 = 2.0
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_index_put_lift_fresh_1(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 = 0.9
    tmp2 = tmp0 < tmp1
    tmp3 = 1.0
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_per_fused_index_put_lift_fresh_sum_2(in_ptr0, out_ptr0, out_ptr1,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = 2.0
    tmp2 = tmp0 == tmp1
    tmp3 = 0.0
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tl.store(out_ptr0 + tl.broadcast_to(r0, [RBLOCK]), tmp4, None)
    tl.store(out_ptr1 + tl.full([1], 0, tl.int32), tmp7, None)


@triton.jit
def triton_poi_fused_div_mul_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (4 * x2 + 16 * (y0 // 4) + 64 * y1 + y0 % 4), 
        xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr2 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, YBLOCK])
    tmp2 = tmp0 * tmp1
    tmp3 = 256.0
    tmp4 = tmp2 * tmp3
    tmp7 = tmp4 / tmp6
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp7, xmask & ymask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 16, 4), (64, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        get_raw_stream(0)
        triton_poi_fused_index_put_lift_fresh_0[grid(256)](arg1_1, arg1_1, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        triton_poi_fused_index_put_lift_fresh_1[grid(256)](arg1_1, arg1_1, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        buf6 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_index_put_lift_fresh_sum_2[grid(1)](arg1_1, arg1_1,
            buf6, 1, 256, num_warps=2, num_stages=1)
        buf7 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        triton_poi_fused_div_mul_3[grid(64, 4)](arg0_1, arg1_1, buf6, buf7,
            64, 4, XBLOCK=4, YBLOCK=64, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
        del buf6
    return reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 1, 4), 0),


class DropBlockT_1dNew(nn.Module):

    def __init__(self, keep_prob=0.9):
        super(DropBlockT_1dNew, self).__init__()
        self.keep_prob = keep_prob

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
