# 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/rr/crrgtc7cjhmconlc4lrghdtlxlrhusyvwnt3b35beyeisdtl2lgu.py
# Topologically Sorted Source Nodes: [fill_], Original ATen: [aten.fill]
# Source node to ATen node mapping:
#   fill_ => full_default
# Graph fragment:
#   %full_default : [num_users=4] = call_function[target=torch.ops.aten.full.default](args = ([4, 1], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
triton_poi_fused_fill_0 = async_compile.triton('triton_poi_fused_fill_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_fill_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_fill_0(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 1.0
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/yn/cyneyfxx44v6cylz6voujw7xntwjaqi4l3utnyjbksdtzfqswrd5.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%view, %full_default], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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=[32], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', '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_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 20
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = (xindex // 5)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 5, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = 1.0
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp6, tmp9, tmp10)
    tmp12 = tl.where(tmp4, tmp5, tmp11)
    tl.store(out_ptr0 + (x2), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/nc/cncd5yvgjpjfrvyaveva55remy3ckx5quvuhdmurdt3pp6k3qtux.py
# Topologically Sorted Source Nodes: [a1], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   a1 => relu
# Graph fragment:
#   %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_3), kwargs = {})
#   %relu : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
triton_poi_fused_relu_2 = async_compile.triton('triton_poi_fused_relu_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=[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_relu_2', '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_relu_2(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 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, 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, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [fill_], Original ATen: [aten.fill]
        stream0 = get_raw_stream(0)
        triton_poi_fused_fill_0.run(buf0, 4, grid=grid(4), stream=stream0)
        buf1 = empty_strided_cuda((4, 5), (5, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(primals_1, buf1, 20, grid=grid(20), stream=stream0)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf2)
        del primals_2
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [a1], Original ATen: [aten.relu]
        triton_poi_fused_relu_2.run(buf3, primals_3, 16, grid=grid(16), stream=stream0)
        del primals_3
        buf4 = empty_strided_cuda((4, 5), (5, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf3, buf4, 20, grid=grid(20), stream=stream0)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf3, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf5)
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [a2], Original ATen: [aten.relu]
        triton_poi_fused_relu_2.run(buf6, primals_5, 16, grid=grid(16), stream=stream0)
        del primals_5
        buf7 = empty_strided_cuda((4, 5), (5, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf6, buf7, 20, grid=grid(20), stream=stream0)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, buf6, reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf8)
        del primals_7
    return (buf8, buf7, buf6, buf4, buf3, buf1, buf0, primals_1, buf3, buf6, 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, 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((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (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
import torch.utils.data


def sample_K_laplace_MN(MAP, upper_Qinv, lower_HHinv):
    Z = MAP.data.new(MAP.size()).normal_(mean=0, std=1)
    all_mtx_sample = MAP + torch.matmul(torch.matmul(lower_HHinv, Z),
        upper_Qinv)
    weight_mtx_sample = all_mtx_sample[:, :-1]
    bias_mtx_sample = all_mtx_sample[:, -1]
    return weight_mtx_sample, bias_mtx_sample


class Linear_2L_KFRA(nn.Module):

    def __init__(self, input_dim, output_dim, n_hid):
        super(Linear_2L_KFRA, self).__init__()
        self.n_hid = n_hid
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.fc1 = nn.Linear(input_dim, self.n_hid)
        self.fc2 = nn.Linear(self.n_hid, self.n_hid)
        self.fc3 = nn.Linear(self.n_hid, output_dim)
        self.act = nn.ReLU(inplace=True)
        self.one = None
        self.a2 = None
        self.h2 = None
        self.a1 = None
        self.h1 = None
        self.a0 = None

    def forward(self, x):
        self.one = x.new(x.shape[0], 1).fill_(1)
        a0 = x.view(-1, self.input_dim)
        self.a0 = torch.cat((a0.data, self.one), dim=1)
        h1 = self.fc1(a0)
        self.h1 = h1.data
        a1 = self.act(h1)
        self.a1 = torch.cat((a1.data, self.one), dim=1)
        h2 = self.fc2(a1)
        self.h2 = h2.data
        a2 = self.act(h2)
        self.a2 = torch.cat((a2.data, self.one), dim=1)
        h3 = self.fc3(a2)
        return h3

    def sample_predict(self, x, Nsamples, Qinv1, HHinv1, MAP1, Qinv2,
        HHinv2, MAP2, Qinv3, HHinv3, MAP3):
        predictions = x.data.new(Nsamples, x.shape[0], self.output_dim)
        x = x.view(-1, self.input_dim)
        for i in range(Nsamples):
            w1, b1 = sample_K_laplace_MN(MAP1, Qinv1, HHinv1)
            a = torch.matmul(x, torch.t(w1)) + b1.unsqueeze(0)
            a = self.act(a)
            w2, b2 = sample_K_laplace_MN(MAP2, Qinv2, HHinv2)
            a = torch.matmul(a, torch.t(w2)) + b2.unsqueeze(0)
            a = self.act(a)
            w3, b3 = sample_K_laplace_MN(MAP3, Qinv3, HHinv3)
            y = torch.matmul(a, torch.t(w3)) + b3.unsqueeze(0)
            predictions[i] = y
        return predictions


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


def get_init_inputs():
    return [[], {'input_dim': 4, 'output_dim': 4, 'n_hid': 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
import torch.nn as nn
import torch.utils.data
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_fill_0(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 1.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 20
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = xindex // 5
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 5, tl.int64)
    tmp9 = 1.0
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp6, tmp9, tmp10)
    tmp12 = tl.where(tmp4, tmp5, tmp11)
    tl.store(out_ptr0 + x2, tmp12, xmask)


@triton.jit
def triton_poi_fused_relu_2(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 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, 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, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_fill_0[grid(4)](buf0, 4, XBLOCK=4, num_warps=1,
            num_stages=1)
        buf1 = empty_strided_cuda((4, 5), (5, 1), torch.float32)
        triton_poi_fused_cat_1[grid(20)](primals_1, buf1, 20, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4),
            (1, 4), 0), out=buf2)
        del primals_2
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_2[grid(16)](buf3, primals_3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((4, 5), (5, 1), torch.float32)
        triton_poi_fused_cat_1[grid(20)](buf3, buf4, 20, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_4, (4, 4), (1, 4
            ), 0), out=buf5)
        buf6 = buf5
        del buf5
        triton_poi_fused_relu_2[grid(16)](buf6, primals_5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_5
        buf7 = empty_strided_cuda((4, 5), (5, 1), torch.float32)
        triton_poi_fused_cat_1[grid(20)](buf6, buf7, 20, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf6, reinterpret_tensor(primals_6,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf8)
        del primals_7
    return (buf8, buf7, buf6, buf4, buf3, buf1, buf0, primals_1, buf3, buf6,
        primals_6, primals_4)


def sample_K_laplace_MN(MAP, upper_Qinv, lower_HHinv):
    Z = MAP.data.new(MAP.size()).normal_(mean=0, std=1)
    all_mtx_sample = MAP + torch.matmul(torch.matmul(lower_HHinv, Z),
        upper_Qinv)
    weight_mtx_sample = all_mtx_sample[:, :-1]
    bias_mtx_sample = all_mtx_sample[:, -1]
    return weight_mtx_sample, bias_mtx_sample


class Linear_2L_KFRANew(nn.Module):

    def __init__(self, input_dim, output_dim, n_hid):
        super(Linear_2L_KFRANew, self).__init__()
        self.n_hid = n_hid
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.fc1 = nn.Linear(input_dim, self.n_hid)
        self.fc2 = nn.Linear(self.n_hid, self.n_hid)
        self.fc3 = nn.Linear(self.n_hid, output_dim)
        self.act = nn.ReLU(inplace=True)
        self.one = None
        self.a2 = None
        self.h2 = None
        self.a1 = None
        self.h1 = None
        self.a0 = None

    def sample_predict(self, x, Nsamples, Qinv1, HHinv1, MAP1, Qinv2,
        HHinv2, MAP2, Qinv3, HHinv3, MAP3):
        predictions = x.data.new(Nsamples, x.shape[0], self.output_dim)
        x = x.view(-1, self.input_dim)
        for i in range(Nsamples):
            w1, b1 = sample_K_laplace_MN(MAP1, Qinv1, HHinv1)
            a = torch.matmul(x, torch.t(w1)) + b1.unsqueeze(0)
            a = self.act(a)
            w2, b2 = sample_K_laplace_MN(MAP2, Qinv2, HHinv2)
            a = torch.matmul(a, torch.t(w2)) + b2.unsqueeze(0)
            a = self.act(a)
            w3, b3 = sample_K_laplace_MN(MAP3, Qinv3, HHinv3)
            y = torch.matmul(a, torch.t(w3)) + b3.unsqueeze(0)
            predictions[i] = y
        return predictions

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