# 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_6/inductor_cache/z5/cz5bgdo2gmhnnmtf6w7lrjkvliacxo7nomq7mbmjquxqyxqgt5bj.py
# Topologically Sorted Source Nodes: [feat], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   feat => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_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_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 2
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/dd/cddxwex7cwfprervbfeycuuplj36x3rh67cnh5kc3z7udvmaw5a5.py
# Topologically Sorted Source Nodes: [mu, mul, l_t, log_scale, sub, pow_2, neg, mul_1, truediv, sub_1, log_pi, log_pi_1], Original ATen: [aten.tanh, aten.mul, aten.add, aten.log, aten.sub, aten.pow, aten.neg, aten.div, aten.sum]
# Source node to ATen node mapping:
#   l_t => add
#   log_pi => sub_2
#   log_pi_1 => sum_1
#   log_scale => full_default
#   mu => tanh
#   mul => mul
#   mul_1 => full_default_1
#   neg => neg
#   pow_2 => pow_2
#   sub => sub
#   sub_1 => sub_1
#   truediv => div
# Graph fragment:
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%view_3,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%normal_functional, %expand), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%tanh, %mul), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4, 4], 1.3862943649291992), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %tanh), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%pow_2,), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4, 4], 32.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%neg, %full_default_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %full_default), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_1, 0.9189385332046727), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sub_2, [1]), kwargs = {})
triton_poi_fused_add_div_log_mul_neg_pow_sub_sum_tanh_1 = async_compile.triton('triton_poi_fused_add_div_log_mul_neg_pow_sub_sum_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=[64], 
    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_div_log_mul_neg_pow_sub_sum_tanh_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_div_log_mul_neg_pow_sub_sum_tanh_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp2 = tl.load(in_ptr1 + (x0 + (64*x1)), xmask)
    tmp15 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp17 = tl.load(in_ptr1 + (16 + x0 + (64*x1)), xmask)
    tmp27 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp29 = tl.load(in_ptr1 + (32 + x0 + (64*x1)), xmask)
    tmp39 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp41 = tl.load(in_ptr1 + (48 + x0 + (64*x1)), xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp3 = 4.0
    tmp4 = tmp2 * tmp3
    tmp5 = tmp1 + tmp4
    tmp6 = tmp5 - tmp1
    tmp7 = tmp6 * tmp6
    tmp8 = -tmp7
    tmp9 = 0.03125
    tmp10 = tmp8 * tmp9
    tmp11 = 1.3862943649291992
    tmp12 = tmp10 - tmp11
    tmp13 = 0.9189385332046727
    tmp14 = tmp12 - tmp13
    tmp16 = libdevice.tanh(tmp15)
    tmp18 = tmp17 * tmp3
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 - tmp16
    tmp21 = tmp20 * tmp20
    tmp22 = -tmp21
    tmp23 = tmp22 * tmp9
    tmp24 = tmp23 - tmp11
    tmp25 = tmp24 - tmp13
    tmp26 = tmp14 + tmp25
    tmp28 = libdevice.tanh(tmp27)
    tmp30 = tmp29 * tmp3
    tmp31 = tmp28 + tmp30
    tmp32 = tmp31 - tmp28
    tmp33 = tmp32 * tmp32
    tmp34 = -tmp33
    tmp35 = tmp34 * tmp9
    tmp36 = tmp35 - tmp11
    tmp37 = tmp36 - tmp13
    tmp38 = tmp26 + tmp37
    tmp40 = libdevice.tanh(tmp39)
    tmp42 = tmp41 * tmp3
    tmp43 = tmp40 + tmp42
    tmp44 = tmp43 - tmp40
    tmp45 = tmp44 * tmp44
    tmp46 = -tmp45
    tmp47 = tmp46 * tmp9
    tmp48 = tmp47 - tmp11
    tmp49 = tmp48 - tmp13
    tmp50 = tmp38 + tmp49
    tl.store(out_ptr0 + (x2), tmp50, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/wa/cwablyx4r3ifgi7p3ugkmqxfsyq2clouhpzo5gpw6wrj2fjncf4s.py
# Topologically Sorted Source Nodes: [mu, mul, l_t, l_t_2], Original ATen: [aten.tanh, aten.mul, aten.add, aten.clamp]
# Source node to ATen node mapping:
#   l_t => add
#   l_t_2 => clamp_max, clamp_min
#   mu => tanh
#   mul => mul
# Graph fragment:
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%view_3,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%normal_functional, %expand), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%tanh, %mul), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add, -1), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1), kwargs = {})
triton_poi_fused_add_clamp_mul_tanh_2 = async_compile.triton('triton_poi_fused_add_clamp_mul_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=[256], 
    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_clamp_mul_tanh_2', '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_clamp_mul_tanh_2(in_ptr0, in_ptr1, 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
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp2 = tl.load(in_ptr1 + (x0), xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp3 = 4.0
    tmp4 = tmp2 * tmp3
    tmp5 = tmp1 + tmp4
    tmp6 = -1.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = 1.0
    tmp9 = triton_helpers.minimum(tmp7, tmp8)
    tl.store(out_ptr0 + (x0), tmp9, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4), (4, 1))
    assert_size_stride(primals_3, (2, ), (1, ))
    assert_size_stride(primals_4, (4, 2), (2, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 2), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 2), (32, 8, 2, 1), 0); del buf0  # reuse
        buf8 = empty_strided_cuda((4, 4, 4, 2), (32, 8, 2, 1), torch.bool)
        # Topologically Sorted Source Nodes: [feat], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_3, buf8, 128, grid=grid(128), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 2), (2, 1), 0), reinterpret_tensor(primals_4, (2, 4), (1, 2), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [eps], Original ATen: [aten.normal_functional]
        buf4 = torch.ops.aten.normal_functional.default(buf3)
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mu, mul, l_t, log_scale, sub, pow_2, neg, mul_1, truediv, sub_1, log_pi, log_pi_1], Original ATen: [aten.tanh, aten.mul, aten.add, aten.log, aten.sub, aten.pow, aten.neg, aten.div, aten.sum]
        triton_poi_fused_add_div_log_mul_neg_pow_sub_sum_tanh_1.run(buf2, buf5, buf6, 64, grid=grid(64), stream=stream0)
        buf7 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [mu, mul, l_t, l_t_2], Original ATen: [aten.tanh, aten.mul, aten.add, aten.clamp]
        triton_poi_fused_add_clamp_mul_tanh_2.run(buf2, buf5, buf7, 256, grid=grid(256), stream=stream0)
    return (buf6, buf7, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 2), (2, 1), 0), buf2, buf5, primals_4, buf8, )


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((2, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 2), (2, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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.nn.functional as F
from torch.distributions import Normal


class LocationNetwork(nn.Module):
    """The location network.

    Uses the internal state `h_t` of the core network to
    produce the location coordinates `l_t` for the next
    time step.

    Concretely, feeds the hidden state `h_t` through a fc
    layer followed by a tanh to clamp the output beween
    [-1, 1]. This produces a 2D vector of means used to
    parametrize a two-component Gaussian with a fixed
    variance from which the location coordinates `l_t`
    for the next time step are sampled.

    Hence, the location `l_t` is chosen stochastically
    from a distribution conditioned on an affine
    transformation of the hidden state vector `h_t`.

    Args:
        input_size: input size of the fc layer.
        output_size: output size of the fc layer.
        std: standard deviation of the normal distribution.
        h_t: the hidden state vector of the core network for
            the current time step `t`.

    Returns:
        mu: a 2D vector of shape (B, 2).
        l_t: a 2D vector of shape (B, 2).
    """

    def __init__(self, input_size, output_size, std):
        super().__init__()
        self.std = std
        hid_size = input_size // 2
        self.fc = nn.Linear(input_size, hid_size)
        self.fc_lt = nn.Linear(hid_size, output_size)

    def forward(self, h_t):
        feat = F.relu(self.fc(h_t.detach()))
        mu = torch.tanh(self.fc_lt(feat))
        l_t = torch.distributions.Normal(mu, self.std).rsample()
        l_t = l_t.detach()
        log_pi = Normal(mu, self.std).log_prob(l_t)
        log_pi = torch.sum(log_pi, dim=1)
        l_t = torch.clamp(l_t, -1, 1)
        return log_pi, l_t


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


def get_init_inputs():
    return [[], {'input_size': 4, 'output_size': 4, 'std': 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
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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 2
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_div_log_mul_neg_pow_sub_sum_tanh_1(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp2 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp17 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp27 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp29 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp39 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp41 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp3 = 4.0
    tmp4 = tmp2 * tmp3
    tmp5 = tmp1 + tmp4
    tmp6 = tmp5 - tmp1
    tmp7 = tmp6 * tmp6
    tmp8 = -tmp7
    tmp9 = 0.03125
    tmp10 = tmp8 * tmp9
    tmp11 = 1.3862943649291992
    tmp12 = tmp10 - tmp11
    tmp13 = 0.9189385332046727
    tmp14 = tmp12 - tmp13
    tmp16 = libdevice.tanh(tmp15)
    tmp18 = tmp17 * tmp3
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 - tmp16
    tmp21 = tmp20 * tmp20
    tmp22 = -tmp21
    tmp23 = tmp22 * tmp9
    tmp24 = tmp23 - tmp11
    tmp25 = tmp24 - tmp13
    tmp26 = tmp14 + tmp25
    tmp28 = libdevice.tanh(tmp27)
    tmp30 = tmp29 * tmp3
    tmp31 = tmp28 + tmp30
    tmp32 = tmp31 - tmp28
    tmp33 = tmp32 * tmp32
    tmp34 = -tmp33
    tmp35 = tmp34 * tmp9
    tmp36 = tmp35 - tmp11
    tmp37 = tmp36 - tmp13
    tmp38 = tmp26 + tmp37
    tmp40 = libdevice.tanh(tmp39)
    tmp42 = tmp41 * tmp3
    tmp43 = tmp40 + tmp42
    tmp44 = tmp43 - tmp40
    tmp45 = tmp44 * tmp44
    tmp46 = -tmp45
    tmp47 = tmp46 * tmp9
    tmp48 = tmp47 - tmp11
    tmp49 = tmp48 - tmp13
    tmp50 = tmp38 + tmp49
    tl.store(out_ptr0 + x2, tmp50, xmask)


@triton.jit
def triton_poi_fused_add_clamp_mul_tanh_2(in_ptr0, in_ptr1, 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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp3 = 4.0
    tmp4 = tmp2 * tmp3
    tmp5 = tmp1 + tmp4
    tmp6 = -1.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = 1.0
    tmp9 = triton_helpers.minimum(tmp7, tmp8)
    tl.store(out_ptr0 + x0, tmp9, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4), (4, 1))
    assert_size_stride(primals_3, (2,), (1,))
    assert_size_stride(primals_4, (4, 2), (2, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 2), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 2), (32, 8, 2, 1), 0)
        del buf0
        buf8 = empty_strided_cuda((4, 4, 4, 2), (32, 8, 2, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(128)](buf1,
            primals_3, buf8, 128, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 2), (
            2, 1), 0), reinterpret_tensor(primals_4, (2, 4), (1, 2), 0),
            alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = torch.ops.aten.normal_functional.default(buf3)
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_log_mul_neg_pow_sub_sum_tanh_1[grid(64)](buf2,
            buf5, buf6, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf7 = buf3
        del buf3
        triton_poi_fused_add_clamp_mul_tanh_2[grid(256)](buf2, buf5, buf7, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
    return buf6, buf7, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 2), (2, 1), 0
        ), buf2, buf5, primals_4, buf8


class LocationNetworkNew(nn.Module):
    """The location network.

    Uses the internal state `h_t` of the core network to
    produce the location coordinates `l_t` for the next
    time step.

    Concretely, feeds the hidden state `h_t` through a fc
    layer followed by a tanh to clamp the output beween
    [-1, 1]. This produces a 2D vector of means used to
    parametrize a two-component Gaussian with a fixed
    variance from which the location coordinates `l_t`
    for the next time step are sampled.

    Hence, the location `l_t` is chosen stochastically
    from a distribution conditioned on an affine
    transformation of the hidden state vector `h_t`.

    Args:
        input_size: input size of the fc layer.
        output_size: output size of the fc layer.
        std: standard deviation of the normal distribution.
        h_t: the hidden state vector of the core network for
            the current time step `t`.

    Returns:
        mu: a 2D vector of shape (B, 2).
        l_t: a 2D vector of shape (B, 2).
    """

    def __init__(self, input_size, output_size, std):
        super().__init__()
        self.std = std
        hid_size = input_size // 2
        self.fc = nn.Linear(input_size, hid_size)
        self.fc_lt = nn.Linear(hid_size, output_size)

    def forward(self, input_0):
        primals_2 = self.fc.weight
        primals_3 = self.fc.bias
        primals_4 = self.fc_lt.weight
        primals_5 = self.fc_lt.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0], output[1]
