# 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/pu/cpu5hzfefz7limmmrr6spzanoriiictxlg4uggegovs7kp7pvgje.py
# Topologically Sorted Source Nodes: [type_1], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   type_1 => convert_element_type
# Graph fragment:
#   %convert_element_type : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%select, torch.int64), kwargs = {})
triton_poi_fused__to_copy_0 = async_compile.triton('triton_poi_fused__to_copy_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_0', '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__to_copy_0(in_ptr0, 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 + (16 + x0), xmask)
    tmp1 = tmp0.to(tl.int64)
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/qq/cqqe2erb265w5u627lkj2yyybu3awp7yhcnwna76rd6eo3g3psq6.py
# Topologically Sorted Source Nodes: [weights], Original ATen: [aten.index]
# Source node to ATen node mapping:
#   weights => index
# Graph fragment:
#   %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%primals_1, [%convert_element_type]), kwargs = {})
triton_poi_fused_index_1 = async_compile.triton('triton_poi_fused_index_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: '*i64', 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_index_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_index_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
    x1 = (xindex // 16)
    x0 = xindex % 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 4")
    tmp6 = tl.load(in_ptr1 + (x0 + (16*tmp4)), xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/c2/cc2gtouifho62qi2kfmn6og6tvij7wy3oi6aqj27a7mmu7yh3nrj.py
# Topologically Sorted Source Nodes: [biases, add], Original ATen: [aten.index, aten.add]
# Source node to ATen node mapping:
#   add => add
#   biases => index_1
# Graph fragment:
#   %index_1 : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%primals_3, [%convert_element_type]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%squeeze, %index_1), kwargs = {})
triton_poi_fused_add_index_2 = async_compile.triton('triton_poi_fused_add_index_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 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_index_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_index_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.full([XBLOCK], 4, tl.int32)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp1 < 0
    tmp5 = tl.where(tmp4, tmp3, tmp1)
    tl.device_assert(((0 <= tmp5) & (tmp5 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp5 < 4")
    tmp7 = tl.load(in_ptr1 + (x0 + (4*tmp5)), xmask)
    tmp8 = tmp0 + tmp7
    tl.store(in_out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (5, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [type_1], Original ATen: [aten._to_copy]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy_0.run(primals_2, buf0, 4, grid=grid(4), stream=stream0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [weights], Original ATen: [aten.index]
        triton_poi_fused_index_1.run(buf0, primals_1, buf1, 64, grid=grid(64), stream=stream0)
        del primals_1
        buf2 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [weights, bmm], Original ATen: [aten.index, aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_4, (4, 1, 4), (4, 4, 1), 0), buf1, out=buf2)
        del buf1
        buf3 = reinterpret_tensor(buf2, (4, 4), (4, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [biases, add], Original ATen: [aten.index, aten.add]
        triton_poi_fused_add_index_2.run(buf3, buf0, primals_3, 16, grid=grid(16), stream=stream0)
        del primals_3
    return (buf3, buf0, reinterpret_tensor(primals_4, (4, 4, 1), (4, 1, 4), 0), )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((5, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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 numpy as np
import torch.nn as nn


def tensor(x, dtype=torch.float32):
    if torch.is_tensor(x):
        return x.type(dtype)
    x = torch.tensor(x, device=Config.DEVICE, dtype=dtype)
    return x


def batch_linear(input, weight, bias=None):
    """ input: (N, D), weight: (N, D, H), bias: (N, H) """
    if bias is not None:
        return torch.bmm(input.unsqueeze(1), weight).squeeze(1) + bias
    else:
        return torch.bmm(input.unsqueeze(1), weight).squeeze(1)


def weight_init(weight, w_scale=1.0):
    init_f = nn.init.orthogonal_
    init_f(weight.data)
    weight.data.mul_(w_scale)
    return weight


class BaseNormalizer:

    def __init__(self, read_only=False):
        self.read_only = read_only

    def set_read_only(self):
        self.read_only = True

    def unset_read_only(self):
        self.read_only = False

    def state_dict(self):
        return None

    def load_state_dict(self, _):
        return


class RescaleNormalizer(BaseNormalizer):

    def __init__(self, coef=1.0):
        BaseNormalizer.__init__(self)
        self.coef = coef

    def __call__(self, x):
        x = np.asarray(x)
        return self.coef * x


class Config:
    DEVICE = torch.device('cpu')

    def __init__(self):
        self.parser = argparse.ArgumentParser()
        self.task_fn = None
        self.optimizer_fn = None
        self.actor_optimizer_fn = None
        self.critic_optimizer_fn = None
        self.network_fn = None
        self.actor_network_fn = None
        self.critic_network_fn = None
        self.replay_fn = None
        self.random_process_fn = None
        self.discount = None
        self.target_network_update_freq = None
        self.exploration_steps = None
        self.logger = None
        self.history_length = None
        self.double_q = False
        self.tag = 'vanilla'
        self.num_workers = 1
        self.gradient_clip = None
        self.entropy_weight = 0
        self.use_gae = False
        self.gae_tau = 1.0
        self.target_network_mix = 0.001
        self.state_normalizer = RescaleNormalizer()
        self.reward_normalizer = RescaleNormalizer()
        self.min_memory_size = None
        self.max_steps = 0
        self.rollout_length = None
        self.value_loss_weight = 1.0
        self.iteration_log_interval = 30
        self.categorical_v_min = None
        self.categorical_v_max = None
        self.categorical_n_atoms = 51
        self.num_quantiles = None
        self.optimization_epochs = 4
        self.mini_batch_size = 64
        self.termination_regularizer = 0
        self.sgd_update_frequency = None
        self.random_action_prob = None
        self.__eval_env = None
        self.log_interval = int(1000.0)
        self.save_interval = 0
        self.eval_interval = 0
        self.eval_episodes = 10
        self.async_actor = True
        self.abs_dim = 512

    @property
    def eval_env(self):
        return self.__eval_env

    @eval_env.setter
    def eval_env(self, env):
        self.__eval_env = env
        self.state_dim = env.state_dim
        self.action_dim = env.action_dim
        self.task_name = env.name

    def add_argument(self, *args, **kwargs):
        self.parser.add_argument(*args, **kwargs)

    def merge(self, config_dict=None):
        if config_dict is None:
            args = self.parser.parse_args()
            config_dict = args.__dict__
        for key in config_dict.keys():
            setattr(self, key, config_dict[key])


class MultiLinear(nn.Module):

    def __init__(self, input_dim, output_dim, n_heads, key, w_scale=1.0):
        super().__init__()
        self.weights = nn.Parameter(weight_init(torch.randn(n_heads,
            input_dim, output_dim), w_scale=w_scale))
        self.biases = nn.Parameter(torch.zeros(n_heads, output_dim))
        self.key = key

    def forward(self, inputs, info):
        weights = self.weights[tensor(info[self.key], torch.int64), :, :]
        biases = self.biases[tensor(info[self.key], torch.int64), :]
        return batch_linear(inputs, weight=weights, bias=biases)

    def get_weight(self, info):
        return self.weights[tensor(info[self.key], torch.int64), :, :]

    def load_weight(self, weight_dict):
        for i, weight in weight_dict.items():
            self.weights.data[i] = weight


def get_inputs():
    return [torch.rand([4, 4]), torch.rand([5, 4])]


def get_init_inputs():
    return [[], {'input_dim': 4, 'output_dim': 4, 'n_heads': 4, 'key': 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
import numpy as np
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__to_copy_0(in_ptr0, 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 + (16 + x0), xmask)
    tmp1 = tmp0.to(tl.int64)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_index_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
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tl.load(in_ptr1 + (x0 + 16 * tmp4), xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_index_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.full([XBLOCK], 4, tl.int32)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp1 < 0
    tmp5 = tl.where(tmp4, tmp3, tmp1)
    tl.device_assert((0 <= tmp5) & (tmp5 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp5 < 4')
    tmp7 = tl.load(in_ptr1 + (x0 + 4 * tmp5), xmask)
    tmp8 = tmp0 + tmp7
    tl.store(in_out_ptr0 + x2, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (5, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4,), (1,), torch.int64)
        get_raw_stream(0)
        triton_poi_fused__to_copy_0[grid(4)](primals_2, buf0, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_index_1[grid(64)](buf0, primals_1, buf1, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_4, (4, 1, 4), (4, 4, 
            1), 0), buf1, out=buf2)
        del buf1
        buf3 = reinterpret_tensor(buf2, (4, 4), (4, 1), 0)
        del buf2
        triton_poi_fused_add_index_2[grid(16)](buf3, buf0, primals_3, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
    return buf3, buf0, reinterpret_tensor(primals_4, (4, 4, 1), (4, 1, 4), 0)


def tensor(x, dtype=torch.float32):
    if torch.is_tensor(x):
        return x.type(dtype)
    x = torch.tensor(x, device=Config.DEVICE, dtype=dtype)
    return x


def batch_linear(input, weight, bias=None):
    """ input: (N, D), weight: (N, D, H), bias: (N, H) """
    if bias is not None:
        return torch.bmm(input.unsqueeze(1), weight).squeeze(1) + bias
    else:
        return torch.bmm(input.unsqueeze(1), weight).squeeze(1)


def weight_init(weight, w_scale=1.0):
    init_f = nn.init.orthogonal_
    init_f(weight.data)
    weight.data.mul_(w_scale)
    return weight


class BaseNormalizer:

    def __init__(self, read_only=False):
        self.read_only = read_only

    def set_read_only(self):
        self.read_only = True

    def unset_read_only(self):
        self.read_only = False

    def state_dict(self):
        return None

    def load_state_dict(self, _):
        return


class RescaleNormalizer(BaseNormalizer):

    def __init__(self, coef=1.0):
        BaseNormalizer.__init__(self)
        self.coef = coef

    def __call__(self, x):
        x = np.asarray(x)
        return self.coef * x


class Config:
    DEVICE = torch.device('cpu')

    def __init__(self):
        self.parser = argparse.ArgumentParser()
        self.task_fn = None
        self.optimizer_fn = None
        self.actor_optimizer_fn = None
        self.critic_optimizer_fn = None
        self.network_fn = None
        self.actor_network_fn = None
        self.critic_network_fn = None
        self.replay_fn = None
        self.random_process_fn = None
        self.discount = None
        self.target_network_update_freq = None
        self.exploration_steps = None
        self.logger = None
        self.history_length = None
        self.double_q = False
        self.tag = 'vanilla'
        self.num_workers = 1
        self.gradient_clip = None
        self.entropy_weight = 0
        self.use_gae = False
        self.gae_tau = 1.0
        self.target_network_mix = 0.001
        self.state_normalizer = RescaleNormalizer()
        self.reward_normalizer = RescaleNormalizer()
        self.min_memory_size = None
        self.max_steps = 0
        self.rollout_length = None
        self.value_loss_weight = 1.0
        self.iteration_log_interval = 30
        self.categorical_v_min = None
        self.categorical_v_max = None
        self.categorical_n_atoms = 51
        self.num_quantiles = None
        self.optimization_epochs = 4
        self.mini_batch_size = 64
        self.termination_regularizer = 0
        self.sgd_update_frequency = None
        self.random_action_prob = None
        self.__eval_env = None
        self.log_interval = int(1000.0)
        self.save_interval = 0
        self.eval_interval = 0
        self.eval_episodes = 10
        self.async_actor = True
        self.abs_dim = 512

    @property
    def eval_env(self):
        return self.__eval_env

    @eval_env.setter
    def eval_env(self, env):
        self.__eval_env = env
        self.state_dim = env.state_dim
        self.action_dim = env.action_dim
        self.task_name = env.name

    def add_argument(self, *args, **kwargs):
        self.parser.add_argument(*args, **kwargs)

    def merge(self, config_dict=None):
        if config_dict is None:
            args = self.parser.parse_args()
            config_dict = args.__dict__
        for key in config_dict.keys():
            setattr(self, key, config_dict[key])


class MultiLinearNew(nn.Module):

    def __init__(self, input_dim, output_dim, n_heads, key, w_scale=1.0):
        super().__init__()
        self.weights = nn.Parameter(weight_init(torch.randn(n_heads,
            input_dim, output_dim), w_scale=w_scale))
        self.biases = nn.Parameter(torch.zeros(n_heads, output_dim))
        self.key = key

    def get_weight(self, info):
        return self.weights[tensor(info[self.key], torch.int64), :, :]

    def load_weight(self, weight_dict):
        for i, weight in weight_dict.items():
            self.weights.data[i] = weight

    def forward(self, input_0, input_1):
        primals_1 = self.weights
        primals_3 = self.biases
        primals_4 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
