def deploy_token(
    deploy_client: JSONRPCClient,
    contract_manager: ContractManager,
    initial_amount: typing.TokenAmount,
    decimals: int,
    token_name: str,
    token_symbol: str,
) -> ContractProxy:
    token_address = deploy_contract_web3(
        contract_name=CONTRACT_HUMAN_STANDARD_TOKEN,
        deploy_client=deploy_client,
        contract_manager=contract_manager,
        constructor_arguments=(initial_amount, decimals, token_name, token_symbol),
    )

    contract_abi = contract_manager.get_contract_abi(CONTRACT_HUMAN_STANDARD_TOKEN)
    return deploy_client.new_contract_proxy(
        contract_interface=contract_abi, contract_address=token_address
    )
def get_attn(attn_type):
    if isinstance(attn_type, torch.nn.Module):
        return attn_type
    module_cls = None
    if attn_type is not None:
        if isinstance(attn_type, str):
            attn_type = attn_type.lower()
            # Lightweight attention modules (channel and/or coarse spatial).
            # Typically added to existing network architecture blocks in addition to existing convolutions.
            if attn_type == 'se':
                module_cls = SEModule
            elif attn_type == 'ese':
                module_cls = EffectiveSEModule
            elif attn_type == 'eca':
                module_cls = EcaModule
            elif attn_type == 'ecam':
                module_cls = partial(EcaModule, use_mlp=True)
            elif attn_type == 'ceca':
                module_cls = CecaModule
            elif attn_type == 'ge':
                module_cls = GatherExcite
            elif attn_type == 'gc':
                module_cls = GlobalContext
            elif attn_type == 'gca':
                module_cls = partial(GlobalContext, fuse_add=True, fuse_scale=False)
            elif attn_type == 'cbam':
                module_cls = CbamModule
            elif attn_type == 'lcbam':
                module_cls = LightCbamModule

            # Attention / attention-like modules w/ significant params
            # Typically replace some of the existing workhorse convs in a network architecture.
            # All of these accept a stride argument and can spatially downsample the input.
            elif attn_type == 'sk':
                module_cls = SelectiveKernel
            elif attn_type == 'splat':
                module_cls = SplitAttn

            # Self-attention / attention-like modules w/ significant compute and/or params
            # Typically replace some of the existing workhorse convs in a network architecture.
            # All of these accept a stride argument and can spatially downsample the input.
            elif attn_type == 'lambda':
                return LambdaLayer
            elif attn_type == 'bottleneck':
                return BottleneckAttn
            elif attn_type == 'halo':
                return HaloAttn
            elif attn_type == 'nl':
                module_cls = NonLocalAttn
            elif attn_type == 'bat':
                module_cls = BatNonLocalAttn

            # Woops!
            else:
                assert False, "Invalid attn module (%s)" % attn_type
        elif isinstance(attn_type, bool):
            if attn_type:
                module_cls = SEModule
        else:
            module_cls = attn_type
    return module_cls