# 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/64/c64ahxnpt5ixqrlolbug3qf6y4u2zqmcjekif2yu4ba4hcze2fom.py
# Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   contiguous => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_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=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_clone_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_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 % 16
    x1 = (xindex // 16) % 4
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x2) + (64*x1)), xmask)
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ep/cepg7n2oamsvuzkzadedwkeu7ogrs3j2mlbo6mx6355lvo4wnsfw.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   x_2 => amax, clone_1, exp, log, sub, sum_1
# Graph fragment:
#   %clone_1 : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute_2,), kwargs = {memory_format: torch.contiguous_format})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%clone_1, [-1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone_1, %amax), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
triton_poi_fused__log_softmax_1 = async_compile.triton('triton_poi_fused__log_softmax_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=[64], 
    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__log_softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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__log_softmax_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (3*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (1 + (3*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1))
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp12 = tl.load(in_ptr0 + (2 + (3*x0)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (2))
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp9 = tmp6 + tmp8
    tmp10 = triton_helpers.maximum(tmp4, tmp9)
    tmp11 = triton_helpers.maximum(tmp5, tmp10)
    tmp15 = tmp12 + tmp14
    tmp16 = triton_helpers.maximum(tmp4, tmp15)
    tmp17 = triton_helpers.maximum(tmp11, tmp16)
    tmp18 = tmp5 - tmp17
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp10 - tmp17
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tmp16 - tmp17
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tl_math.log(tmp25)
    tl.store(out_ptr0 + (x0), tmp17, xmask)
    tl.store(out_ptr1 + (x0), tmp26, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/6k/c6k5t4mz5tbxecw6oybhcegftl7sqqh4qu335irzyzfu2ydr5bnc.py
# Topologically Sorted Source Nodes: [y_1, x_2], Original ATen: [aten.relu, aten._log_softmax, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_2 => amax, clone_1, exp, log, sub, sub_1, sum_1
#   y_1 => relu
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_3), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
#   %clone_1 : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute_2,), kwargs = {memory_format: torch.contiguous_format})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%clone_1, [-1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone_1, %amax), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused__log_softmax_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused__log_softmax_relu_threshold_backward_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: '*i1', 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__log_softmax_relu_threshold_backward_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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__log_softmax_relu_threshold_backward_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 3
    x3 = (xindex // 3)
    x1 = (xindex // 3) % 16
    x2 = (xindex // 48)
    tmp0 = tl.load(in_ptr0 + (x4), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x3), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + (x3), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp4 - tmp5
    tmp8 = tmp6 - tmp7
    tmp9 = 0.0
    tmp10 = tmp4 <= tmp9
    tl.store(out_ptr0 + (x0 + (3*x2) + (12*x1)), tmp8, xmask)
    tl.store(out_ptr1 + (x4), tmp10, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (3, 4), (4, 1))
    assert_size_stride(primals_3, (3, ), (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)
        # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(primals_1, buf0, 256, grid=grid(256), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((64, 3), (3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 3), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((16, 4, 1), (1, 16, 64), torch.float32)
        buf3 = empty_strided_cuda((16, 4, 1), (1, 16, 64), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._log_softmax]
        triton_poi_fused__log_softmax_1.run(buf1, primals_3, buf2, buf3, 64, grid=grid(64), stream=stream0)
        buf4 = empty_strided_cuda((16, 4, 3), (12, 3, 1), torch.float32)
        buf5 = empty_strided_cuda((64, 3), (3, 1), torch.bool)
        # Topologically Sorted Source Nodes: [y_1, x_2], Original ATen: [aten.relu, aten._log_softmax, aten.threshold_backward]
        triton_poi_fused__log_softmax_relu_threshold_backward_2.run(buf1, primals_3, buf2, buf3, buf4, buf5, 192, grid=grid(192), stream=stream0)
        del buf1
        del buf2
        del buf3
        del primals_3
    return (buf4, reinterpret_tensor(buf0, (64, 4), (4, 1), 0), buf4, buf5, )


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((3, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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)

from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn


class TimeDistributed(nn.Module):

    def __init__(self, layer, activation='relu'):
        super().__init__()
        self.layer = layer
        self.activation = self.select_activation(activation)

    def forward(self, x):
        x_reshaped = x.contiguous().view(-1, x.size(-1))
        y = self.layer(x_reshaped)
        y = self.activation(y)
        y = y.contiguous().view(x.size(0), -1, y.size(-1))
        return y

    def select_activation(self, activation):
        if activation == 'relu':
            return nn.ReLU()
        elif activation == 'sigmoid':
            return nn.Sigmoid()
        elif activation == 'tanh':
            return nn.Tanh()
        raise KeyError


class Projection(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.seq_fnn = TimeDistributed(nn.Linear(config['cnn_features'], 3))
        self.softmax = nn.LogSoftmax(dim=-1)

    def forward(self, x):
        x = torch.transpose(x, 1, 0)
        x = self.seq_fnn(x)
        x = torch.transpose(x, 1, 0)
        x = self.softmax(x)
        return x


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


def get_init_inputs():
    return [[], {'config': _mock_config(cnn_features=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 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_clone_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 % 16
    x1 = xindex // 16 % 4
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2 + 64 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused__log_softmax_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 3 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (1 + 3 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + 1)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp12 = tl.load(in_ptr0 + (2 + 3 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr1 + 2)
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp9 = tmp6 + tmp8
    tmp10 = triton_helpers.maximum(tmp4, tmp9)
    tmp11 = triton_helpers.maximum(tmp5, tmp10)
    tmp15 = tmp12 + tmp14
    tmp16 = triton_helpers.maximum(tmp4, tmp15)
    tmp17 = triton_helpers.maximum(tmp11, tmp16)
    tmp18 = tmp5 - tmp17
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp10 - tmp17
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tmp16 - tmp17
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tl_math.log(tmp25)
    tl.store(out_ptr0 + x0, tmp17, xmask)
    tl.store(out_ptr1 + x0, tmp26, xmask)


@triton.jit
def triton_poi_fused__log_softmax_relu_threshold_backward_2(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 3
    x3 = xindex // 3
    x1 = xindex // 3 % 16
    x2 = xindex // 48
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + x3, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp4 - tmp5
    tmp8 = tmp6 - tmp7
    tmp9 = 0.0
    tmp10 = tmp4 <= tmp9
    tl.store(out_ptr0 + (x0 + 3 * x2 + 12 * x1), tmp8, xmask)
    tl.store(out_ptr1 + x4, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (3, 4), (4, 1))
    assert_size_stride(primals_3, (3,), (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_clone_0[grid(256)](primals_1, buf0, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 3), (3, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 3), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((16, 4, 1), (1, 16, 64), torch.float32)
        buf3 = empty_strided_cuda((16, 4, 1), (1, 16, 64), torch.float32)
        triton_poi_fused__log_softmax_1[grid(64)](buf1, primals_3, buf2,
            buf3, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((16, 4, 3), (12, 3, 1), torch.float32)
        buf5 = empty_strided_cuda((64, 3), (3, 1), torch.bool)
        triton_poi_fused__log_softmax_relu_threshold_backward_2[grid(192)](buf1
            , primals_3, buf2, buf3, buf4, buf5, 192, XBLOCK=128, num_warps
            =4, num_stages=1)
        del buf1
        del buf2
        del buf3
        del primals_3
    return buf4, reinterpret_tensor(buf0, (64, 4), (4, 1), 0), buf4, buf5


class TimeDistributed(nn.Module):

    def __init__(self, layer, activation='relu'):
        super().__init__()
        self.layer = layer
        self.activation = self.select_activation(activation)

    def forward(self, x):
        x_reshaped = x.contiguous().view(-1, x.size(-1))
        y = self.layer(x_reshaped)
        y = self.activation(y)
        y = y.contiguous().view(x.size(0), -1, y.size(-1))
        return y

    def select_activation(self, activation):
        if activation == 'relu':
            return nn.ReLU()
        elif activation == 'sigmoid':
            return nn.Sigmoid()
        elif activation == 'tanh':
            return nn.Tanh()
        raise KeyError


class ProjectionNew(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.seq_fnn = TimeDistributed(nn.Linear(config['cnn_features'], 3))
        self.softmax = nn.LogSoftmax(dim=-1)

    def forward(self, input_0):
        primals_2 = self.seq_fnn.layer.weight
        primals_3 = self.seq_fnn.layer.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
