# 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/jw/cjwwa4uchk246nlrjndt3n65ojf447r3xpsci6idv5lw24heuhdo.py
# Topologically Sorted Source Nodes: [pointz], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   pointz => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_2, %repeat], 2), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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: '*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_cat_0', '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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x3 = (xindex // 8)
    x2 = (xindex // 32)
    x4 = 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*x3) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + ((4*x2) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x4), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/64/c64lll6yinoxu3btcjtxh5rzioifpmotbj4afx3qjyfw5mary2ck.py
# Topologically Sorted Source Nodes: [l1_1], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
# Source node to ATen node mapping:
#   l1_1 => gt, mul, where
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_2, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, 0.02), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_2, %mul), kwargs = {})
#   %gt_13 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_7, 0), kwargs = {})
triton_poi_fused_leaky_relu_leaky_relu_backward_1 = async_compile.triton('triton_poi_fused_leaky_relu_leaky_relu_backward_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=[512], 
    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_leaky_relu_leaky_relu_backward_1', '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_leaky_relu_leaky_relu_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.02
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp7 > tmp3
    tl.store(in_out_ptr0 + (x2), tmp7, xmask)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ti/cti22bxg4qsfbng3v7u4raerj5lzoh5a64j3em4764gdrc465h7d.py
# Topologically Sorted Source Nodes: [l2], Original ATen: [aten.view]
# Source node to ATen node mapping:
#   l2 => view_8
# Graph fragment:
#   %view_8 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%view_7, [16, 32]), kwargs = {})
triton_poi_fused_view_2 = async_compile.triton('triton_poi_fused_view_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=[512], 
    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_view_2', '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_view_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 32
    x1 = (xindex // 32)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (32*x1) + (128*((x1 % 4) // 4))), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/6t/c6tusshowwdbxf5nsxmyx5dcxuulu6vtbhmy3jw3vupevgtaedyu.py
# Topologically Sorted Source Nodes: [l4_1], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
# Source node to ATen node mapping:
#   l4_1 => gt_3, mul_3, where_3
# Graph fragment:
#   %gt_3 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_23, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_23, 0.02), kwargs = {})
#   %where_3 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %view_23, %mul_3), kwargs = {})
#   %gt_10 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_28, 0), kwargs = {})
triton_poi_fused_leaky_relu_leaky_relu_backward_3 = async_compile.triton('triton_poi_fused_leaky_relu_leaky_relu_backward_3', '''
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: '*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_leaky_relu_leaky_relu_backward_3', '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_leaky_relu_leaky_relu_backward_3(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.02
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp7 > tmp3
    tl.store(in_out_ptr0 + (x2), tmp7, xmask)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/7z/c7zm4tyr3rypvx65fueqp3r24bqlv7vks4rliqjgjetfbvbhj5h7.py
# Topologically Sorted Source Nodes: [l5], Original ATen: [aten.view]
# Source node to ATen node mapping:
#   l5 => view_29
# Graph fragment:
#   %view_29 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%view_28, [16, 16]), kwargs = {})
triton_poi_fused_view_4 = async_compile.triton('triton_poi_fused_view_4', '''
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: '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_view_4', '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_view_4(in_ptr0, 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 % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*((x1 % 4) // 4))), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/h5/ch5nxovctuncjebc2nqs7pnug7krzpkxodacd3jupvkgcjmek6ym.py
# Topologically Sorted Source Nodes: [l5_1], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
# Source node to ATen node mapping:
#   l5_1 => gt_4, mul_4, where_4
# Graph fragment:
#   %gt_4 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_30, 0), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_30, 0.02), kwargs = {})
#   %where_4 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_4, %view_30, %mul_4), kwargs = {})
#   %gt_9 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_35, 0), kwargs = {})
triton_poi_fused_leaky_relu_leaky_relu_backward_5 = async_compile.triton('triton_poi_fused_leaky_relu_leaky_relu_backward_5', '''
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_leaky_relu_leaky_relu_backward_5', '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_leaky_relu_leaky_relu_backward_5(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 % 8
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.02
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp7 > tmp3
    tl.store(in_out_ptr0 + (x2), tmp7, xmask)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/5a/c5adurbyuwonzbthlahvnenpae6eylytblwuhpxx7zxxne7cpkqg.py
# Topologically Sorted Source Nodes: [l6], Original ATen: [aten.view]
# Source node to ATen node mapping:
#   l6 => view_36
# Graph fragment:
#   %view_36 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%view_35, [16, 8]), kwargs = {})
triton_poi_fused_view_6 = async_compile.triton('triton_poi_fused_view_6', '''
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: '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_view_6', '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_view_6(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
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (8*x1) + (32*((x1 % 4) // 4))), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/z2/cz2sspfuxbnoc4p75z7grw4k5v3rc4yl5ogsmjsy3aukwaahbjib.py
# Topologically Sorted Source Nodes: [l6_1], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
# Source node to ATen node mapping:
#   l6_1 => gt_5, mul_5, where_5
# Graph fragment:
#   %gt_5 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_37, 0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_37, 0.02), kwargs = {})
#   %where_5 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_5, %view_37, %mul_5), kwargs = {})
#   %gt_8 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_42, 0), kwargs = {})
triton_poi_fused_leaky_relu_leaky_relu_backward_7 = async_compile.triton('triton_poi_fused_leaky_relu_leaky_relu_backward_7', '''
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: '*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_leaky_relu_leaky_relu_backward_7', '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_leaky_relu_leaky_relu_backward_7(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    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 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.02
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp7 > tmp3
    tl.store(in_out_ptr0 + (x2), tmp7, xmask)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/nk/cnkg43ztbtoykl4eicljla57hdvc4w25zoro3v6tin275gwyfdez.py
# Topologically Sorted Source Nodes: [l7], Original ATen: [aten.view]
# Source node to ATen node mapping:
#   l7 => view_43
# Graph fragment:
#   %view_43 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%view_42, [16, 4]), kwargs = {})
triton_poi_fused_view_8 = async_compile.triton('triton_poi_fused_view_8', '''
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: '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_view_8', '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_view_8(in_ptr0, 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 % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x1) + (16*((x1 % 4) // 4))), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ai/caiddvmwqbea5d3qrc33js62trj3rjp7hvodxgfbkzf3zqwowbu3.py
# Topologically Sorted Source Nodes: [mul, add, min_1, l7_1], Original ATen: [aten.mul, aten.add, aten.minimum, aten.maximum]
# Source node to ATen node mapping:
#   add => add
#   l7_1 => maximum
#   min_1 => minimum
#   mul => mul_6
# Graph fragment:
#   %mul_6 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_44, 0.01), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_6, 0.99), kwargs = {})
#   %minimum : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%view_44, %add), kwargs = {})
#   %maximum : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%minimum, %mul_6), kwargs = {})
triton_poi_fused_add_maximum_minimum_mul_9 = async_compile.triton('triton_poi_fused_add_maximum_minimum_mul_9', '''
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_maximum_minimum_mul_9', '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_add_maximum_minimum_mul_9(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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 = 0.01
    tmp2 = tmp0 * tmp1
    tmp3 = 0.99
    tmp4 = tmp2 + tmp3
    tmp5 = triton_helpers.minimum(tmp0, tmp4)
    tmp6 = triton_helpers.maximum(tmp5, tmp2)
    tl.store(out_ptr0 + (x0), tmp6, 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, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (32, 8), (8, 1))
    assert_size_stride(primals_4, (32, ), (1, ))
    assert_size_stride(primals_5, (32, 32), (32, 1))
    assert_size_stride(primals_6, (32, ), (1, ))
    assert_size_stride(primals_7, (32, 32), (32, 1))
    assert_size_stride(primals_8, (32, ), (1, ))
    assert_size_stride(primals_9, (16, 32), (32, 1))
    assert_size_stride(primals_10, (16, ), (1, ))
    assert_size_stride(primals_11, (8, 16), (16, 1))
    assert_size_stride(primals_12, (8, ), (1, ))
    assert_size_stride(primals_13, (4, 8), (8, 1))
    assert_size_stride(primals_14, (4, ), (1, ))
    assert_size_stride(primals_15, (1, 4), (4, 1))
    assert_size_stride(primals_16, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pointz], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_2, primals_1, buf0, 128, grid=grid(128), stream=stream0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 8), (8, 1), 0), reinterpret_tensor(primals_3, (8, 32), (1, 8), 0), out=buf1)
        del primals_3
        buf2 = reinterpret_tensor(buf1, (4, 4, 32), (128, 32, 1), 0); del buf1  # reuse
        buf27 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [l1_1], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_leaky_relu_leaky_relu_backward_1.run(buf2, primals_4, buf27, 512, grid=grid(512), stream=stream0)
        del primals_4
        buf3 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [l2], Original ATen: [aten.view]
        triton_poi_fused_view_2.run(buf2, buf3, 512, grid=grid(512), stream=stream0)
        buf4 = reinterpret_tensor(buf2, (16, 32), (32, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf3, reinterpret_tensor(primals_5, (32, 32), (1, 32), 0), out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 32), (128, 32, 1), 0); del buf4  # reuse
        buf26 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [l2_1], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_leaky_relu_leaky_relu_backward_1.run(buf5, primals_6, buf26, 512, grid=grid(512), stream=stream0)
        del primals_6
        buf6 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [l3], Original ATen: [aten.view]
        triton_poi_fused_view_2.run(buf5, buf6, 512, grid=grid(512), stream=stream0)
        buf7 = reinterpret_tensor(buf5, (16, 32), (32, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf6, reinterpret_tensor(primals_7, (32, 32), (1, 32), 0), out=buf7)
        buf8 = reinterpret_tensor(buf7, (4, 4, 32), (128, 32, 1), 0); del buf7  # reuse
        buf25 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [l3_1], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_leaky_relu_leaky_relu_backward_1.run(buf8, primals_8, buf25, 512, grid=grid(512), stream=stream0)
        del primals_8
        buf9 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [l4], Original ATen: [aten.view]
        triton_poi_fused_view_2.run(buf8, buf9, 512, grid=grid(512), stream=stream0)
        del buf8
        buf10 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf9, reinterpret_tensor(primals_9, (32, 16), (1, 32), 0), out=buf10)
        buf11 = reinterpret_tensor(buf10, (4, 4, 16), (64, 16, 1), 0); del buf10  # reuse
        buf24 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.bool)
        # Topologically Sorted Source Nodes: [l4_1], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_leaky_relu_leaky_relu_backward_3.run(buf11, primals_10, buf24, 256, grid=grid(256), stream=stream0)
        del primals_10
        buf12 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [l5], Original ATen: [aten.view]
        triton_poi_fused_view_4.run(buf11, buf12, 256, grid=grid(256), stream=stream0)
        del buf11
        buf13 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf12, reinterpret_tensor(primals_11, (16, 8), (1, 16), 0), out=buf13)
        buf14 = reinterpret_tensor(buf13, (4, 4, 8), (32, 8, 1), 0); del buf13  # reuse
        buf23 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [l5_1], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_leaky_relu_leaky_relu_backward_5.run(buf14, primals_12, buf23, 128, grid=grid(128), stream=stream0)
        del primals_12
        buf15 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [l6], Original ATen: [aten.view]
        triton_poi_fused_view_6.run(buf14, buf15, 128, grid=grid(128), stream=stream0)
        del buf14
        buf16 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf15, reinterpret_tensor(primals_13, (8, 4), (1, 8), 0), out=buf16)
        buf17 = reinterpret_tensor(buf16, (4, 4, 4), (16, 4, 1), 0); del buf16  # reuse
        buf22 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [l6_1], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_leaky_relu_leaky_relu_backward_7.run(buf17, primals_14, buf22, 64, grid=grid(64), stream=stream0)
        del primals_14
        buf18 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [l7], Original ATen: [aten.view]
        triton_poi_fused_view_8.run(buf17, buf18, 64, grid=grid(64), stream=stream0)
        del buf17
        buf20 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [l7], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_16, buf18, reinterpret_tensor(primals_15, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf20)
        del primals_16
        buf21 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, add, min_1, l7_1], Original ATen: [aten.mul, aten.add, aten.minimum, aten.maximum]
        triton_poi_fused_add_maximum_minimum_mul_9.run(buf20, buf21, 16, grid=grid(16), stream=stream0)
    return (buf21, reinterpret_tensor(buf0, (16, 8), (8, 1), 0), buf3, buf6, buf9, buf12, buf15, buf18, buf20, primals_15, buf22, primals_13, buf23, primals_11, buf24, primals_9, buf25, primals_7, buf26, primals_5, buf27, )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((32, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((32, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((16, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((8, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16])
    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


class generator(nn.Module):

    def __init__(self, z_dim, point_dim, gf_dim):
        super(generator, self).__init__()
        self.z_dim = z_dim
        self.point_dim = point_dim
        self.gf_dim = gf_dim
        self.linear_1 = nn.Linear(self.z_dim + self.point_dim, self.gf_dim *
            8, bias=True)
        self.linear_2 = nn.Linear(self.gf_dim * 8, self.gf_dim * 8, bias=True)
        self.linear_3 = nn.Linear(self.gf_dim * 8, self.gf_dim * 8, bias=True)
        self.linear_4 = nn.Linear(self.gf_dim * 8, self.gf_dim * 4, bias=True)
        self.linear_5 = nn.Linear(self.gf_dim * 4, self.gf_dim * 2, bias=True)
        self.linear_6 = nn.Linear(self.gf_dim * 2, self.gf_dim * 1, bias=True)
        self.linear_7 = nn.Linear(self.gf_dim * 1, 1, bias=True)
        nn.init.normal_(self.linear_1.weight, mean=0.0, std=0.02)
        nn.init.constant_(self.linear_1.bias, 0)
        nn.init.normal_(self.linear_2.weight, mean=0.0, std=0.02)
        nn.init.constant_(self.linear_2.bias, 0)
        nn.init.normal_(self.linear_3.weight, mean=0.0, std=0.02)
        nn.init.constant_(self.linear_3.bias, 0)
        nn.init.normal_(self.linear_4.weight, mean=0.0, std=0.02)
        nn.init.constant_(self.linear_4.bias, 0)
        nn.init.normal_(self.linear_5.weight, mean=0.0, std=0.02)
        nn.init.constant_(self.linear_5.bias, 0)
        nn.init.normal_(self.linear_6.weight, mean=0.0, std=0.02)
        nn.init.constant_(self.linear_6.bias, 0)
        nn.init.normal_(self.linear_7.weight, mean=1e-05, std=0.02)
        nn.init.constant_(self.linear_7.bias, 0)

    def forward(self, points, z, is_training=False):
        zs = z.view(-1, 1, self.z_dim).repeat(1, points.size()[1], 1)
        pointz = torch.cat([points, zs], 2)
        l1 = self.linear_1(pointz)
        l1 = F.leaky_relu(l1, negative_slope=0.02, inplace=True)
        l2 = self.linear_2(l1)
        l2 = F.leaky_relu(l2, negative_slope=0.02, inplace=True)
        l3 = self.linear_3(l2)
        l3 = F.leaky_relu(l3, negative_slope=0.02, inplace=True)
        l4 = self.linear_4(l3)
        l4 = F.leaky_relu(l4, negative_slope=0.02, inplace=True)
        l5 = self.linear_5(l4)
        l5 = F.leaky_relu(l5, negative_slope=0.02, inplace=True)
        l6 = self.linear_6(l5)
        l6 = F.leaky_relu(l6, negative_slope=0.02, inplace=True)
        l7 = self.linear_7(l6)
        l7 = torch.max(torch.min(l7, l7 * 0.01 + 0.99), l7 * 0.01)
        return l7


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


def get_init_inputs():
    return [[], {'z_dim': 4, 'point_dim': 4, 'gf_dim': 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
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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x3 = xindex // 8
    x2 = xindex // 32
    x4 = 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 * x3 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x2 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x4, tmp10, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_leaky_relu_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.02
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp7 > tmp3
    tl.store(in_out_ptr0 + x2, tmp7, xmask)
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_view_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 32
    x1 = xindex // 32
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 32 * x1 + 128 * (x1 % 4 // 4)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_leaky_relu_backward_3(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.02
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp7 > tmp3
    tl.store(in_out_ptr0 + x2, tmp7, xmask)
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_view_4(in_ptr0, 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 % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * (x1 % 4 // 4)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_leaky_relu_backward_5(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 % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.02
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp7 > tmp3
    tl.store(in_out_ptr0 + x2, tmp7, xmask)
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_view_6(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
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1 + 32 * (x1 % 4 // 4)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_leaky_relu_backward_7(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    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 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.02
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp7 > tmp3
    tl.store(in_out_ptr0 + x2, tmp7, xmask)
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_view_8(in_ptr0, 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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * (x1 % 4 // 4)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_maximum_minimum_mul_9(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    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 = 0.01
    tmp2 = tmp0 * tmp1
    tmp3 = 0.99
    tmp4 = tmp2 + tmp3
    tmp5 = triton_helpers.minimum(tmp0, tmp4)
    tmp6 = triton_helpers.maximum(tmp5, tmp2)
    tl.store(out_ptr0 + x0, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (32, 8), (8, 1))
    assert_size_stride(primals_4, (32,), (1,))
    assert_size_stride(primals_5, (32, 32), (32, 1))
    assert_size_stride(primals_6, (32,), (1,))
    assert_size_stride(primals_7, (32, 32), (32, 1))
    assert_size_stride(primals_8, (32,), (1,))
    assert_size_stride(primals_9, (16, 32), (32, 1))
    assert_size_stride(primals_10, (16,), (1,))
    assert_size_stride(primals_11, (8, 16), (16, 1))
    assert_size_stride(primals_12, (8,), (1,))
    assert_size_stride(primals_13, (4, 8), (8, 1))
    assert_size_stride(primals_14, (4,), (1,))
    assert_size_stride(primals_15, (1, 4), (4, 1))
    assert_size_stride(primals_16, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(128)](primals_2, primals_1, buf0, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 8), (8, 1), 0),
            reinterpret_tensor(primals_3, (8, 32), (1, 8), 0), out=buf1)
        del primals_3
        buf2 = reinterpret_tensor(buf1, (4, 4, 32), (128, 32, 1), 0)
        del buf1
        buf27 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        triton_poi_fused_leaky_relu_leaky_relu_backward_1[grid(512)](buf2,
            primals_4, buf27, 512, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        triton_poi_fused_view_2[grid(512)](buf2, buf3, 512, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf4 = reinterpret_tensor(buf2, (16, 32), (32, 1), 0)
        del buf2
        extern_kernels.mm(buf3, reinterpret_tensor(primals_5, (32, 32), (1,
            32), 0), out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 32), (128, 32, 1), 0)
        del buf4
        buf26 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        triton_poi_fused_leaky_relu_leaky_relu_backward_1[grid(512)](buf5,
            primals_6, buf26, 512, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_6
        buf6 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        triton_poi_fused_view_2[grid(512)](buf5, buf6, 512, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf5, (16, 32), (32, 1), 0)
        del buf5
        extern_kernels.mm(buf6, reinterpret_tensor(primals_7, (32, 32), (1,
            32), 0), out=buf7)
        buf8 = reinterpret_tensor(buf7, (4, 4, 32), (128, 32, 1), 0)
        del buf7
        buf25 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        triton_poi_fused_leaky_relu_leaky_relu_backward_1[grid(512)](buf8,
            primals_8, buf25, 512, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_8
        buf9 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        triton_poi_fused_view_2[grid(512)](buf8, buf9, 512, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf8
        buf10 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(buf9, reinterpret_tensor(primals_9, (32, 16), (1,
            32), 0), out=buf10)
        buf11 = reinterpret_tensor(buf10, (4, 4, 16), (64, 16, 1), 0)
        del buf10
        buf24 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.bool)
        triton_poi_fused_leaky_relu_leaky_relu_backward_3[grid(256)](buf11,
            primals_10, buf24, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_10
        buf12 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        triton_poi_fused_view_4[grid(256)](buf11, buf12, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf11
        buf13 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        extern_kernels.mm(buf12, reinterpret_tensor(primals_11, (16, 8), (1,
            16), 0), out=buf13)
        buf14 = reinterpret_tensor(buf13, (4, 4, 8), (32, 8, 1), 0)
        del buf13
        buf23 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.bool)
        triton_poi_fused_leaky_relu_leaky_relu_backward_5[grid(128)](buf14,
            primals_12, buf23, 128, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_12
        buf15 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        triton_poi_fused_view_6[grid(128)](buf14, buf15, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf14
        buf16 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf15, reinterpret_tensor(primals_13, (8, 4), (1,
            8), 0), out=buf16)
        buf17 = reinterpret_tensor(buf16, (4, 4, 4), (16, 4, 1), 0)
        del buf16
        buf22 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_leaky_relu_leaky_relu_backward_7[grid(64)](buf17,
            primals_14, buf22, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_14
        buf18 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        triton_poi_fused_view_8[grid(64)](buf17, buf18, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf17
        buf20 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_16, buf18, reinterpret_tensor(
            primals_15, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf20)
        del primals_16
        buf21 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        triton_poi_fused_add_maximum_minimum_mul_9[grid(16)](buf20, buf21, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
    return (buf21, reinterpret_tensor(buf0, (16, 8), (8, 1), 0), buf3, buf6,
        buf9, buf12, buf15, buf18, buf20, primals_15, buf22, primals_13,
        buf23, primals_11, buf24, primals_9, buf25, primals_7, buf26,
        primals_5, buf27)


class generatorNew(nn.Module):

    def __init__(self, z_dim, point_dim, gf_dim):
        super(generatorNew, self).__init__()
        self.z_dim = z_dim
        self.point_dim = point_dim
        self.gf_dim = gf_dim
        self.linear_1 = nn.Linear(self.z_dim + self.point_dim, self.gf_dim *
            8, bias=True)
        self.linear_2 = nn.Linear(self.gf_dim * 8, self.gf_dim * 8, bias=True)
        self.linear_3 = nn.Linear(self.gf_dim * 8, self.gf_dim * 8, bias=True)
        self.linear_4 = nn.Linear(self.gf_dim * 8, self.gf_dim * 4, bias=True)
        self.linear_5 = nn.Linear(self.gf_dim * 4, self.gf_dim * 2, bias=True)
        self.linear_6 = nn.Linear(self.gf_dim * 2, self.gf_dim * 1, bias=True)
        self.linear_7 = nn.Linear(self.gf_dim * 1, 1, bias=True)
        nn.init.normal_(self.linear_1.weight, mean=0.0, std=0.02)
        nn.init.constant_(self.linear_1.bias, 0)
        nn.init.normal_(self.linear_2.weight, mean=0.0, std=0.02)
        nn.init.constant_(self.linear_2.bias, 0)
        nn.init.normal_(self.linear_3.weight, mean=0.0, std=0.02)
        nn.init.constant_(self.linear_3.bias, 0)
        nn.init.normal_(self.linear_4.weight, mean=0.0, std=0.02)
        nn.init.constant_(self.linear_4.bias, 0)
        nn.init.normal_(self.linear_5.weight, mean=0.0, std=0.02)
        nn.init.constant_(self.linear_5.bias, 0)
        nn.init.normal_(self.linear_6.weight, mean=0.0, std=0.02)
        nn.init.constant_(self.linear_6.bias, 0)
        nn.init.normal_(self.linear_7.weight, mean=1e-05, std=0.02)
        nn.init.constant_(self.linear_7.bias, 0)

    def forward(self, input_0, input_1):
        primals_3 = self.linear_1.weight
        primals_4 = self.linear_1.bias
        primals_5 = self.linear_2.weight
        primals_6 = self.linear_2.bias
        primals_7 = self.linear_3.weight
        primals_8 = self.linear_3.bias
        primals_9 = self.linear_4.weight
        primals_10 = self.linear_4.bias
        primals_11 = self.linear_5.weight
        primals_12 = self.linear_5.bias
        primals_13 = self.linear_6.weight
        primals_14 = self.linear_6.bias
        primals_15 = self.linear_7.weight
        primals_16 = self.linear_7.bias
        primals_2 = input_0
        primals_1 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16])
        return output[0]
