# 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/cu/ccutvo2v4333pq6xhrg2zryqqwthm7dmmuqprvva2xdwiodpz5jn.py
# Topologically Sorted Source Nodes: [output], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   output => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=[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_convolution_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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ca/ccawxivjls6nn6fmdbkmz5aqdguiri4khvzuwx6zynye6gpiaja5.py
# Topologically Sorted Source Nodes: [conv1d_1, output_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv1d_1 => convolution_1
#   output_1 => relu
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution, %primals_4, %primals_5, [1], [0], [1], False, [0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_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: '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_convolution_relu_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_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 20
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), 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 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/h2/ch27hkeznyxjvkqd6cqrzfsuls4s54ertvhjwcfmksi4fv53c6gm.py
# Topologically Sorted Source Nodes: [conv1d_3, output_3], Original ATen: [aten.convolution, aten.sum]
# Source node to ATen node mapping:
#   conv1d_3 => convolution_3
#   output_3 => sum_1
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_8, %primals_9, [1], [0], [1], False, [0], 1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%convolution_3, [2]), kwargs = {})
triton_poi_fused_convolution_sum_2 = async_compile.triton('triton_poi_fused_convolution_sum_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_convolution_sum_2(in_ptr0, in_ptr1, 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 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tmp0 + tmp2
    tmp5 = tmp4 + tmp2
    tmp6 = tmp3 + tmp5
    tmp8 = tmp7 + tmp2
    tmp9 = tmp6 + tmp8
    tmp11 = tmp10 + tmp2
    tmp12 = tmp9 + tmp11
    tl.store(out_ptr0 + (x0), tmp12, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (20, 4, 1), (4, 1, 1))
    assert_size_stride(primals_5, (20, ), (1, ))
    assert_size_stride(primals_6, (20, 20, 1), (20, 1, 1))
    assert_size_stride(primals_7, (20, ), (1, ))
    assert_size_stride(primals_8, (1, 20, 1), (20, 1, 1))
    assert_size_stride(primals_9, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4), (16, 4, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_2, 64, grid=grid(64), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 20, 4), (80, 4, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [conv1d_1, output_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf3, primals_5, 320, grid=grid(320), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [conv1d_2], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf4, (4, 20, 4), (80, 4, 1))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [conv1d_2, output_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf5, primals_7, 320, grid=grid(320), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [conv1d_3], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf6, (4, 1, 4), (4, 4, 1))
        buf7 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv1d_3, output_3], Original ATen: [aten.convolution, aten.sum]
        triton_poi_fused_convolution_sum_2.run(buf6, primals_9, buf7, 4, grid=grid(4), stream=stream0)
        del buf6
        del primals_9
    return (buf7, primals_1, primals_3, primals_4, primals_6, primals_8, buf1, buf3, buf5, )


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, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((20, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((20, 20, 1), (20, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1, 20, 1), (20, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = 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])
    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 Encoder(nn.Module):
    """Encodes the static & dynamic states using 1d Convolution."""

    def __init__(self, input_size, hidden_size):
        super(Encoder, self).__init__()
        self.conv = nn.Conv1d(input_size, hidden_size, kernel_size=1)

    def forward(self, input):
        output = self.conv(input)
        return output


class Critic(nn.Module):
    """Estimates the problem complexity.

    This is a basic module that just looks at the log-probabilities predicted by
    the encoder + decoder, and returns an estimate of complexity
    """

    def __init__(self, dynamic_size, hidden_size):
        super(Critic, self).__init__()
        self.dynamic_encoder = Encoder(dynamic_size, hidden_size)
        self.fc1 = nn.Conv1d(hidden_size, 20, kernel_size=1)
        self.fc2 = nn.Conv1d(20, 20, kernel_size=1)
        self.fc3 = nn.Conv1d(20, 1, kernel_size=1)
        for p in self.parameters():
            if len(p.shape) > 1:
                nn.init.xavier_uniform_(p)

    def forward(self, dynamic):
        dynamic_hidden = self.dynamic_encoder(dynamic)
        output = F.relu(self.fc1(dynamic_hidden))
        output = F.relu(self.fc2(output))
        output = self.fc3(output).sum(dim=2)
        return output


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


def get_init_inputs():
    return [[], {'dynamic_size': 4, 'hidden_size': 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


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 20
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, 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 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_sum_2(in_ptr0, in_ptr1, 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 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tmp0 + tmp2
    tmp5 = tmp4 + tmp2
    tmp6 = tmp3 + tmp5
    tmp8 = tmp7 + tmp2
    tmp9 = tmp6 + tmp8
    tmp11 = tmp10 + tmp2
    tmp12 = tmp9 + tmp11
    tl.store(out_ptr0 + x0, tmp12, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (20, 4, 1), (4, 1, 1))
    assert_size_stride(primals_5, (20,), (1,))
    assert_size_stride(primals_6, (20, 20, 1), (20, 1, 1))
    assert_size_stride(primals_7, (20,), (1,))
    assert_size_stride(primals_8, (1, 20, 1), (20, 1, 1))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4), (16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(64)](buf1, primals_2, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 20, 4), (80, 4, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(320)](buf3, primals_5, 320,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf4, (4, 20, 4), (80, 4, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_1[grid(320)](buf5, primals_7, 320,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf6, (4, 1, 4), (4, 4, 1))
        buf7 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        triton_poi_fused_convolution_sum_2[grid(4)](buf6, primals_9, buf7, 
            4, XBLOCK=4, num_warps=1, num_stages=1)
        del buf6
        del primals_9
    return (buf7, primals_1, primals_3, primals_4, primals_6, primals_8,
        buf1, buf3, buf5)


class Encoder(nn.Module):
    """Encodes the static & dynamic states using 1d Convolution."""

    def __init__(self, input_size, hidden_size):
        super(Encoder, self).__init__()
        self.conv = nn.Conv1d(input_size, hidden_size, kernel_size=1)

    def forward(self, input):
        output = self.conv(input)
        return output


class CriticNew(nn.Module):
    """Estimates the problem complexity.

    This is a basic module that just looks at the log-probabilities predicted by
    the encoder + decoder, and returns an estimate of complexity
    """

    def __init__(self, dynamic_size, hidden_size):
        super(CriticNew, self).__init__()
        self.dynamic_encoder = Encoder(dynamic_size, hidden_size)
        self.fc1 = nn.Conv1d(hidden_size, 20, kernel_size=1)
        self.fc2 = nn.Conv1d(20, 20, kernel_size=1)
        self.fc3 = nn.Conv1d(20, 1, kernel_size=1)
        for p in self.parameters():
            if len(p.shape) > 1:
                nn.init.xavier_uniform_(p)

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