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 % 50
    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)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (50, 4), (4, 1))
    assert_size_stride(primals_2, (50,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (50, 50), (50, 1))
    assert_size_stride(primals_5, (50,), (1,))
    assert_size_stride(primals_6, (50, 50), (50, 1))
    assert_size_stride(primals_7, (50,), (1,))
    assert_size_stride(primals_8, (1, 50), (50, 1))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 50), (50, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 50), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 50), (800, 200, 50, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(3200)](buf1, primals_2, 3200, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 50), (50, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 50), (50, 1), 0),
            reinterpret_tensor(primals_4, (50, 50), (1, 50), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 50), (800, 200, 50, 1), 0)
        del buf2
        triton_poi_fused_tanh_0[grid(3200)](buf3, primals_5, 3200, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 50), (50, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 50), (50, 1), 0),
            reinterpret_tensor(primals_6, (50, 50), (1, 50), 0), out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 50), (800, 200, 50, 1), 0)
        del buf4
        triton_poi_fused_tanh_0[grid(3200)](buf5, primals_7, 3200, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_7
        buf7 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf5, (64, 50),
            (50, 1), 0), reinterpret_tensor(primals_8, (50, 1), (1, 50), 0),
            alpha=1, beta=1, out=buf7)
        del primals_9
    return reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf1, buf3, buf5, primals_8, primals_6, primals_4


class NetNew(nn.Module):

    def __init__(self, n_inputs, n_units=[50, 50, 50]):
        super(NetNew, self).__init__()
        self.fc1 = nn.Linear(n_inputs, n_units[0])
        self.fc2 = nn.Linear(n_units[0], n_units[1])
        self.fc3 = nn.Linear(n_units[1], n_units[2])
        self.out = nn.Linear(n_units[2], 1)

    def basis_funcs(self, x, bias=False, linear=False):
        raw_x = x
        x = torch.tanh(self.fc1(x))
        x = torch.tanh(self.fc2(x))
        x = torch.tanh(self.fc3(x))
        if linear:
            x = torch.cat((x, raw_x), dim=-1)
        if bias:
            x = torch.cat((x, torch.ones(size=(raw_x.shape[0], 1))), dim=-1)
        return x

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_8 = self.out.weight
        primals_9 = self.out.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.015