# 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_8/inductor_cache/5t/c5tyhlk67qhsvexxq6ew4swntclr2csyb4zbg5gfvur7jufhvk4n.py
# Topologically Sorted Source Nodes: [hidden], Original ATen: [aten.tanh]
# Source node to ATen node mapping:
#   hidden => tanh
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_3), kwargs = {})
#   %tanh : [num_users=13] = call_function[target=torch.ops.aten.tanh.default](args = (%add_tensor,), kwargs = {})
triton_poi_fused_tanh_0 = async_compile.triton('triton_poi_fused_tanh_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=[4096], 
    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_tanh_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_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 100
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + (x2), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/uo/cuoadam5vr6fhzma2fnfxnaxckee4rmerx4ndkky5y77nwwewaef.py
# Topologically Sorted Source Nodes: [tanh_1, add, hidden_1], Original ATen: [aten.tanh, aten.add]
# Source node to ATen node mapping:
#   add => add
#   hidden_1 => add_1
#   tanh_1 => tanh_1
# Graph fragment:
#   %tanh_1 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%addmm_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%tanh, %tanh_1), kwargs = {})
#   %add_1 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %tanh), kwargs = {})
triton_poi_fused_add_tanh_1 = async_compile.triton('triton_poi_fused_add_tanh_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=[4096], 
    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_add_tanh_1', 'mutated_arg_names': [], '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_add_tanh_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3200
    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.load(in_ptr1 + (x0), xmask)
    tmp2 = libdevice.tanh(tmp1)
    tmp3 = tmp0 + tmp2
    tmp4 = tmp3 + tmp0
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/nc/cncgfkkpyrri4em47zh5vdzphsb5uohcwrlz3t6sfnrwbnvihm46.py
# Topologically Sorted Source Nodes: [tanh_3, add_2, hidden_2], Original ATen: [aten.tanh, aten.add]
# Source node to ATen node mapping:
#   add_2 => add_2
#   hidden_2 => add_3
#   tanh_3 => tanh_3
# Graph fragment:
#   %tanh_3 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%addmm_3,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %tanh_3), kwargs = {})
#   %add_3 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %tanh), kwargs = {})
triton_poi_fused_add_tanh_2 = async_compile.triton('triton_poi_fused_add_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=[4096], 
    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_add_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_add_tanh_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3200
    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.load(in_ptr1 + (x0), xmask)
    tmp4 = tl.load(in_ptr2 + (x0), xmask)
    tmp2 = libdevice.tanh(tmp1)
    tmp3 = tmp0 + tmp2
    tmp5 = tmp3 + tmp4
    tl.store(out_ptr0 + (x0), tmp5, 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, (100, 8), (8, 1))
    assert_size_stride(primals_3, (100, ), (1, ))
    assert_size_stride(primals_4, (100, 100), (100, 1))
    assert_size_stride(primals_5, (100, ), (1, ))
    assert_size_stride(primals_6, (10, 100), (100, 1))
    assert_size_stride(primals_7, (10, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (32, 8), (8, 1), 0), reinterpret_tensor(primals_2, (8, 100), (1, 8), 0), out=buf0)
        del primals_2
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [hidden], Original ATen: [aten.tanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_tanh_0.run(buf1, primals_3, 3200, grid=grid(3200), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4, (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf2)
        buf3 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh_1, add, hidden_1], Original ATen: [aten.tanh, aten.add]
        triton_poi_fused_add_tanh_1.run(buf1, buf2, buf3, 3200, grid=grid(3200), stream=stream0)
        buf4 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf3, reinterpret_tensor(primals_4, (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf4)
        buf5 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh_3, add_2, hidden_2], Original ATen: [aten.tanh, aten.add]
        triton_poi_fused_add_tanh_2.run(buf3, buf4, buf1, buf5, 3200, grid=grid(3200), stream=stream0)
        buf6 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_5], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf5, reinterpret_tensor(primals_4, (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf6)
        buf7 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh_5, add_4, hidden_3], Original ATen: [aten.tanh, aten.add]
        triton_poi_fused_add_tanh_2.run(buf5, buf6, buf1, buf7, 3200, grid=grid(3200), stream=stream0)
        buf8 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_7], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf7, reinterpret_tensor(primals_4, (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf8)
        buf9 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh_7, add_6, hidden_4], Original ATen: [aten.tanh, aten.add]
        triton_poi_fused_add_tanh_2.run(buf7, buf8, buf1, buf9, 3200, grid=grid(3200), stream=stream0)
        buf10 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_9], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf9, reinterpret_tensor(primals_4, (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf10)
        buf11 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh_9, add_8, hidden_5], Original ATen: [aten.tanh, aten.add]
        triton_poi_fused_add_tanh_2.run(buf9, buf10, buf1, buf11, 3200, grid=grid(3200), stream=stream0)
        buf12 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_11], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf11, reinterpret_tensor(primals_4, (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf12)
        buf13 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh_11, add_10, hidden_6], Original ATen: [aten.tanh, aten.add]
        triton_poi_fused_add_tanh_2.run(buf11, buf12, buf1, buf13, 3200, grid=grid(3200), stream=stream0)
        buf14 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_13], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf13, reinterpret_tensor(primals_4, (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf14)
        buf15 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh_13, add_12, hidden_7], Original ATen: [aten.tanh, aten.add]
        triton_poi_fused_add_tanh_2.run(buf13, buf14, buf1, buf15, 3200, grid=grid(3200), stream=stream0)
        buf16 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_15], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf15, reinterpret_tensor(primals_4, (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf16)
        buf17 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh_15, add_14, hidden_8], Original ATen: [aten.tanh, aten.add]
        triton_poi_fused_add_tanh_2.run(buf15, buf16, buf1, buf17, 3200, grid=grid(3200), stream=stream0)
        buf18 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_17], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf17, reinterpret_tensor(primals_4, (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf18)
        buf19 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh_17, add_16, hidden_9], Original ATen: [aten.tanh, aten.add]
        triton_poi_fused_add_tanh_2.run(buf17, buf18, buf1, buf19, 3200, grid=grid(3200), stream=stream0)
        buf20 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_19], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf19, reinterpret_tensor(primals_4, (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf20)
        del primals_5
        buf21 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh_19, add_18, hidden_10], Original ATen: [aten.tanh, aten.add]
        triton_poi_fused_add_tanh_2.run(buf19, buf20, buf1, buf21, 3200, grid=grid(3200), stream=stream0)
        buf22 = empty_strided_cuda((32, 10), (10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, buf21, reinterpret_tensor(primals_6, (100, 10), (1, 100), 0), alpha=1, beta=1, out=buf22)
        del primals_7
    return (buf22, reinterpret_tensor(primals_1, (32, 8), (8, 1), 0), buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, buf9, buf10, buf11, buf12, buf13, buf14, buf15, buf16, buf17, buf18, buf19, buf20, buf21, primals_6, primals_4, )


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((100, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((100, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((100, 100), (100, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((100, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((10, 100), (100, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((10, ), (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.nn as nn


class ModelBasic(nn.Module):
    """parallel passing of data, categorical output with one unit per number of clusters"""

    def __init__(self, n_obs, n_units=100, n_timesteps=10, max_K=10):
        super(ModelBasic, self).__init__()
        self.fc_input = nn.Linear(2 * n_obs, n_units)
        self.fc_output = nn.Linear(n_units, max_K)
        self.fc_recurrent = nn.Linear(n_units, n_units)
        self.n_obs = n_obs
        self.n_units = n_units
        self.n_timesteps = n_timesteps
        self.max_K = max_K

    def forward(self, x):
        inp = x.view(-1, 2 * self.n_obs)
        hidden = torch.tanh(self.fc_input(inp))
        for i_time in range(self.n_timesteps):
            hidden = hidden + torch.tanh(self.fc_recurrent(hidden)
                ) + torch.tanh(self.fc_input(inp))
        out = self.fc_output(hidden)
        return out

    def init_weights(self):
        nn.init.xavier_uniform_(self.fc_input.weight)
        nn.init.xavier_uniform_(self.fc_output.weight)
        nn.init.xavier_uniform_(self.fc_recurrent.weight)
        self.fc_input.bias.data.fill_(0)
        self.fc_output.bias.data.fill_(0)
        self.fc_recurrent.bias.data.fill_(0)


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


def get_init_inputs():
    return [[], {'n_obs': 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.triton_helpers import libdevice
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_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 3200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 100
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_add_tanh_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 3200
    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.load(in_ptr1 + x0, xmask)
    tmp2 = libdevice.tanh(tmp1)
    tmp3 = tmp0 + tmp2
    tmp4 = tmp3 + tmp0
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_tanh_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 3200
    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.load(in_ptr1 + x0, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = libdevice.tanh(tmp1)
    tmp3 = tmp0 + tmp2
    tmp5 = tmp3 + tmp4
    tl.store(out_ptr0 + x0, tmp5, 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, (100, 8), (8, 1))
    assert_size_stride(primals_3, (100,), (1,))
    assert_size_stride(primals_4, (100, 100), (100, 1))
    assert_size_stride(primals_5, (100,), (1,))
    assert_size_stride(primals_6, (10, 100), (100, 1))
    assert_size_stride(primals_7, (10,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (32, 8), (8, 1), 0),
            reinterpret_tensor(primals_2, (8, 100), (1, 8), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(3200)](buf1, primals_3, 3200, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
            (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf2)
        buf3 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        triton_poi_fused_add_tanh_1[grid(3200)](buf1, buf2, buf3, 3200,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf3, reinterpret_tensor(primals_4,
            (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf4)
        buf5 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        triton_poi_fused_add_tanh_2[grid(3200)](buf3, buf4, buf1, buf5, 
            3200, XBLOCK=256, num_warps=4, num_stages=1)
        buf6 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf5, reinterpret_tensor(primals_4,
            (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf6)
        buf7 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        triton_poi_fused_add_tanh_2[grid(3200)](buf5, buf6, buf1, buf7, 
            3200, XBLOCK=256, num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf7, reinterpret_tensor(primals_4,
            (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf8)
        buf9 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        triton_poi_fused_add_tanh_2[grid(3200)](buf7, buf8, buf1, buf9, 
            3200, XBLOCK=256, num_warps=4, num_stages=1)
        buf10 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf9, reinterpret_tensor(primals_4,
            (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf10)
        buf11 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        triton_poi_fused_add_tanh_2[grid(3200)](buf9, buf10, buf1, buf11, 
            3200, XBLOCK=256, num_warps=4, num_stages=1)
        buf12 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf11, reinterpret_tensor(primals_4,
            (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf12)
        buf13 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        triton_poi_fused_add_tanh_2[grid(3200)](buf11, buf12, buf1, buf13, 
            3200, XBLOCK=256, num_warps=4, num_stages=1)
        buf14 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf13, reinterpret_tensor(primals_4,
            (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf14)
        buf15 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        triton_poi_fused_add_tanh_2[grid(3200)](buf13, buf14, buf1, buf15, 
            3200, XBLOCK=256, num_warps=4, num_stages=1)
        buf16 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf15, reinterpret_tensor(primals_4,
            (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf16)
        buf17 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        triton_poi_fused_add_tanh_2[grid(3200)](buf15, buf16, buf1, buf17, 
            3200, XBLOCK=256, num_warps=4, num_stages=1)
        buf18 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf17, reinterpret_tensor(primals_4,
            (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf18)
        buf19 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        triton_poi_fused_add_tanh_2[grid(3200)](buf17, buf18, buf1, buf19, 
            3200, XBLOCK=256, num_warps=4, num_stages=1)
        buf20 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf19, reinterpret_tensor(primals_4,
            (100, 100), (1, 100), 0), alpha=1, beta=1, out=buf20)
        del primals_5
        buf21 = empty_strided_cuda((32, 100), (100, 1), torch.float32)
        triton_poi_fused_add_tanh_2[grid(3200)](buf19, buf20, buf1, buf21, 
            3200, XBLOCK=256, num_warps=4, num_stages=1)
        buf22 = empty_strided_cuda((32, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf21, reinterpret_tensor(primals_6,
            (100, 10), (1, 100), 0), alpha=1, beta=1, out=buf22)
        del primals_7
    return (buf22, reinterpret_tensor(primals_1, (32, 8), (8, 1), 0), buf1,
        buf2, buf3, buf4, buf5, buf6, buf7, buf8, buf9, buf10, buf11, buf12,
        buf13, buf14, buf15, buf16, buf17, buf18, buf19, buf20, buf21,
        primals_6, primals_4)


class ModelBasicNew(nn.Module):
    """parallel passing of data, categorical output with one unit per number of clusters"""

    def __init__(self, n_obs, n_units=100, n_timesteps=10, max_K=10):
        super(ModelBasicNew, self).__init__()
        self.fc_input = nn.Linear(2 * n_obs, n_units)
        self.fc_output = nn.Linear(n_units, max_K)
        self.fc_recurrent = nn.Linear(n_units, n_units)
        self.n_obs = n_obs
        self.n_units = n_units
        self.n_timesteps = n_timesteps
        self.max_K = max_K

    def init_weights(self):
        nn.init.xavier_uniform_(self.fc_input.weight)
        nn.init.xavier_uniform_(self.fc_output.weight)
        nn.init.xavier_uniform_(self.fc_recurrent.weight)
        self.fc_input.bias.data.fill_(0)
        self.fc_output.bias.data.fill_(0)
        self.fc_recurrent.bias.data.fill_(0)

    def forward(self, input_0):
        primals_2 = self.fc_input.weight
        primals_3 = self.fc_input.bias
        primals_6 = self.fc_output.weight
        primals_7 = self.fc_output.bias
        primals_4 = self.fc_recurrent.weight
        primals_5 = self.fc_recurrent.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
