def test_double_index(subset_func, subset_func2):
    adata = gen_adata((10, 10))
    obs_subset = subset_func(adata.obs_names)
    var_subset = subset_func2(adata.var_names)
    v1 = adata[obs_subset, var_subset]
    v2 = adata[obs_subset, :][:, var_subset]

    assert np.all(asarray(v1.X) == asarray(v2.X))
    assert np.all(v1.obs == v2.obs)
    assert np.all(v1.var == v2.var)
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