# 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_7/inductor_cache/et/cetlocvlmaoxaj6afumfhalegfvgarx6k3pkajf2n7xt7gaq7uwu.py
# Topologically Sorted Source Nodes: [repeat, add, h_prime], Original ATen: [aten.repeat, aten.add, aten.tanh]
# Source node to ATen node mapping:
#   add => add
#   h_prime => tanh
#   repeat => repeat
# Graph fragment:
#   %repeat : [num_users=1] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_3, [4, 1]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm, %repeat), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
triton_poi_fused_add_repeat_tanh_0 = async_compile.triton('triton_poi_fused_add_repeat_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=[16], 
    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_add_repeat_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_add_repeat_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    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_7/inductor_cache/yn/cynjrtcbvvngz6apfumshziobdiji5wn46qzzyqagqifi5yu2zwu.py
# Topologically Sorted Source Nodes: [semantic_attentions_1, semantic_attentions_2], Original ATen: [aten.mean, aten._softmax]
# Source node to ATen node mapping:
#   semantic_attentions_1 => mean
#   semantic_attentions_2 => amax, div, exp, sub, sum_1
# Graph fragment:
#   %mean : [num_users=2] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [1], True), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mean, [0], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mean, %amax), kwargs = {})
#   %exp : [num_users=2] = 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, [0], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_per_fused__softmax_mean_1 = async_compile.triton('triton_per_fused__softmax_mean_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.persistent_reduction(
    size_hints=[1, 2],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_mean_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 2, '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}
)
@triton.jit
def triton_per_fused__softmax_mean_1(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 2
    RBLOCK: tl.constexpr = 2
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (2*r0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*r0)), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 2.0
    tmp4 = tmp2 / tmp3
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = triton_helpers.max2(tmp5, 1)[:, None]
    tmp8 = tmp4 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.sum(tmp10, 1)[:, None]
    tmp13 = tmp9 / tmp12
    tl.store(out_ptr2 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp13, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/tn/ctnlap35mqlgzpamfq2vy76nqnjxkge3sqjgil2a4jlghhnvm7iy.py
# Topologically Sorted Source Nodes: [semantic_attentions_4, h_embedding, sum_1, h_embedding_1], Original ATen: [aten.repeat, aten.mul, aten.sum, aten.squeeze]
# Source node to ATen node mapping:
#   h_embedding => mul
#   h_embedding_1 => squeeze
#   semantic_attentions_4 => repeat_1
#   sum_1 => sum_2
# Graph fragment:
#   %repeat_1 : [num_users=1] = call_function[target=torch.ops.aten.repeat.default](args = (%view_1, [1, 2, 4]), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %repeat_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [0]), kwargs = {})
#   %squeeze : [num_users=1] = call_function[target=torch.ops.aten.squeeze.default](args = (%sum_2,), kwargs = {})
triton_poi_fused_mul_repeat_squeeze_sum_2 = async_compile.triton('triton_poi_fused_mul_repeat_squeeze_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.pointwise(
    size_hints=[8], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_repeat_squeeze_sum_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_mul_repeat_squeeze_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    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 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp5 = tl.load(in_ptr1 + (1))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp3 = tmp0 * tmp2
    tmp7 = tmp4 * tmp6
    tmp8 = tmp3 + tmp7
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (1, 4), (4, 1))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.mm]
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [repeat, add, h_prime], Original ATen: [aten.repeat, aten.add, aten.tanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_repeat_tanh_0.run(buf1, primals_3, 16, grid=grid(16), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mm_1], Original ATen: [aten.mm]
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), out=buf2)
        buf5 = empty_strided_cuda((2, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [semantic_attentions_1, semantic_attentions_2], Original ATen: [aten.mean, aten._softmax]
        triton_per_fused__softmax_mean_1.run(buf2, buf5, 1, 2, grid=grid(1), stream=stream0)
        del buf2
        buf6 = empty_strided_cuda((2, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [semantic_attentions_4, h_embedding, sum_1, h_embedding_1], Original ATen: [aten.repeat, aten.mul, aten.sum, aten.squeeze]
        triton_poi_fused_mul_repeat_squeeze_sum_2.run(primals_2, buf5, buf6, 8, grid=grid(8), stream=stream0)
    return (buf6, buf5, primals_2, buf1, buf5, 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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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.functional as F
import torch.nn as nn


class GraphLevelAttentionLayer(nn.Module):
    """
    Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
    """

    def __init__(self, in_features, out_features):
        super(GraphLevelAttentionLayer, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.my_coefs = None
        self.W = nn.Parameter(torch.zeros(size=(in_features, out_features)))
        nn.init.xavier_uniform_(self.W.data, gain=1.414)
        self.b = nn.Parameter(torch.zeros(size=(1, out_features)))
        nn.init.xavier_uniform_(self.b.data, gain=1.414)
        self.q = nn.Parameter(torch.zeros(size=(1, out_features)))
        nn.init.xavier_uniform_(self.q.data, gain=1.414)
        self.Tanh = nn.Tanh()
        self.leakyrelu = nn.LeakyReLU()

    def forward(self, total_embeds, P=2):
        h = torch.mm(total_embeds, self.W)
        h_prime = self.Tanh(h + self.b.repeat(h.size()[0], 1))
        semantic_attentions = torch.mm(h_prime, torch.t(self.q)).view(P, -1)
        N = semantic_attentions.size()[1]
        semantic_attentions = semantic_attentions.mean(dim=1, keepdim=True)
        semantic_attentions = F.softmax(semantic_attentions, dim=0)
        self.my_coefs = semantic_attentions
        semantic_attentions = semantic_attentions.view(P, 1, 1)
        semantic_attentions = semantic_attentions.repeat(1, N, self.in_features
            )
        input_embedding = total_embeds.view(P, N, self.in_features)
        h_embedding = torch.mul(input_embedding, semantic_attentions)
        h_embedding = torch.sum(h_embedding, dim=0).squeeze()
        return h_embedding

    def __repr__(self):
        return self.__class__.__name__ + ' (' + str(self.in_features
            ) + ' -> ' + str(self.out_features) + ')'


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


def get_init_inputs():
    return [[], {'in_features': 4, 'out_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 libdevice, 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_add_repeat_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    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_per_fused__softmax_mean_1(in_ptr0, out_ptr2, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 2
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + 2 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 2.0
    tmp4 = tmp2 / tmp3
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = triton_helpers.max2(tmp5, 1)[:, None]
    tmp8 = tmp4 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.sum(tmp10, 1)[:, None]
    tmp13 = tmp9 / tmp12
    tl.store(out_ptr2 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp13, None)


@triton.jit
def triton_poi_fused_mul_repeat_squeeze_sum_2(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    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 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp5 = tl.load(in_ptr1 + 1)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp3 = tmp0 * tmp2
    tmp7 = tmp4 * tmp6
    tmp8 = tmp3 + tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (1, 4), (4, 1))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_repeat_tanh_0[grid(16)](buf1, primals_3, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4, 1), (1, 4
            ), 0), out=buf2)
        buf5 = empty_strided_cuda((2, 1), (1, 1), torch.float32)
        triton_per_fused__softmax_mean_1[grid(1)](buf2, buf5, 1, 2, XBLOCK=
            1, num_warps=2, num_stages=1)
        del buf2
        buf6 = empty_strided_cuda((2, 4), (4, 1), torch.float32)
        triton_poi_fused_mul_repeat_squeeze_sum_2[grid(8)](primals_2, buf5,
            buf6, 8, XBLOCK=8, num_warps=1, num_stages=1)
    return buf6, buf5, primals_2, buf1, buf5, primals_4


class GraphLevelAttentionLayerNew(nn.Module):
    """
    Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
    """

    def __init__(self, in_features, out_features):
        super(GraphLevelAttentionLayerNew, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.my_coefs = None
        self.W = nn.Parameter(torch.zeros(size=(in_features, out_features)))
        nn.init.xavier_uniform_(self.W.data, gain=1.414)
        self.b = nn.Parameter(torch.zeros(size=(1, out_features)))
        nn.init.xavier_uniform_(self.b.data, gain=1.414)
        self.q = nn.Parameter(torch.zeros(size=(1, out_features)))
        nn.init.xavier_uniform_(self.q.data, gain=1.414)
        self.Tanh = nn.Tanh()
        self.leakyrelu = nn.LeakyReLU()

    def __repr__(self):
        return self.__class__.__name__ + ' (' + str(self.in_features
            ) + ' -> ' + str(self.out_features) + ')'

    def forward(self, input_0):
        primals_1 = self.W
        primals_3 = self.b
        primals_4 = self.q
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
