How to use the anndata.AnnData.concatenate function in anndata

all_shared_ann = anndata.AnnData.concatenate(*shared_ct, batch_key="concat_batch", index_unique=None)
    if "concat_batch" in all_shared_ann.obs.columns:
        del all_shared_ann.obs["concat_batch"]
    if len(not_shared_ct) < 1:
        corrected = anndata.AnnData(network.reconstruct(all_shared_ann.X, use_data=True))
        corrected.obs = all_shared_ann.obs.copy(deep=True)
        corrected.var_names = adata.var_names.tolist()
        corrected = corrected[adata.obs_names]
        if adata.raw is not None:
            adata_raw = anndata.AnnData(X=adata.raw.X, var=adata.raw.var)
            adata_raw.obs_names = adata.obs_names
            corrected.raw = adata_raw
        corrected.obsm["latent"] = all_shared_ann.X
        return corrected
    else:
        all_not_shared_ann = anndata.AnnData.concatenate(*not_shared_ct, batch_key="concat_batch", index_unique=None)
        all_corrected_data = anndata.AnnData.concatenate(all_shared_ann, all_not_shared_ann, batch_key="concat_batch", index_unique=None)
        if "concat_batch" in all_shared_ann.obs.columns:
            del all_corrected_data.obs["concat_batch"]
        corrected = anndata.AnnData(network.reconstruct(all_corrected_data.X, use_data=True))
        corrected.obs = pd.concat([all_shared_ann.obs, all_not_shared_ann.obs])
        corrected.var_names = adata.var_names.tolist()
        corrected = corrected[adata.obs_names]
        if adata.raw is not None:
            adata_raw = anndata.AnnData(X=adata.raw.X, var=adata.raw.var)
            adata_raw.obs_names = adata.obs_names
            corrected.raw = adata_raw
        corrected.obsm["latent"] = all_corrected_data.X
        return corrected

batches = np.unique(temp_cell.obs[batch_key])
        for i in batches:
            temp = temp_cell[temp_cell.obs[batch_key] == i]
            temp_ind = temp_cell.obs[batch_key] == i
            if max_batch < len(temp):
                max_batch = len(temp)
                max_batch_ind = i
            batch_list[i] = temp
            batch_ind[i] = temp_ind
        max_batch_ann = batch_list[max_batch_ind]
        for study in batch_list:
            delta = np.average(max_batch_ann.X, axis=0) - np.average(batch_list[study].X, axis=0)
            batch_list[study].X = delta + batch_list[study].X
            temp_cell[batch_ind[study]].X = batch_list[study].X
        shared_ct.append(temp_cell)
    all_shared_ann = anndata.AnnData.concatenate(*shared_ct, batch_key="concat_batch", index_unique=None)
    if "concat_batch" in all_shared_ann.obs.columns:
        del all_shared_ann.obs["concat_batch"]
    if len(not_shared_ct) < 1:
        corrected = anndata.AnnData(network.reconstruct(all_shared_ann.X, use_data=True))
        corrected.obs = all_shared_ann.obs.copy(deep=True)
        corrected.var_names = adata.var_names.tolist()
        corrected = corrected[adata.obs_names]
        if adata.raw is not None:
            adata_raw = anndata.AnnData(X=adata.raw.X, var=adata.raw.var)
            adata_raw.obs_names = adata.obs_names
            corrected.raw = adata_raw
        corrected.obsm["latent"] = all_shared_ann.X
        return corrected
    else:
        all_not_shared_ann = anndata.AnnData.concatenate(*not_shared_ct, batch_key="concat_batch", index_unique=None)
        all_corrected_data = anndata.AnnData.concatenate(all_shared_ann, all_not_shared_ann, batch_key="concat_batch", index_unique=None)

if "concat_batch" in all_shared_ann.obs.columns:
        del all_shared_ann.obs["concat_batch"]
    if len(not_shared_ct) < 1:
        corrected = anndata.AnnData(network.reconstruct(all_shared_ann.X, use_data=True))
        corrected.obs = all_shared_ann.obs.copy(deep=True)
        corrected.var_names = adata.var_names.tolist()
        corrected = corrected[adata.obs_names]
        if adata.raw is not None:
            adata_raw = anndata.AnnData(X=adata.raw.X, var=adata.raw.var)
            adata_raw.obs_names = adata.obs_names
            corrected.raw = adata_raw
        corrected.obsm["latent"] = all_shared_ann.X
        return corrected
    else:
        all_not_shared_ann = anndata.AnnData.concatenate(*not_shared_ct, batch_key="concat_batch", index_unique=None)
        all_corrected_data = anndata.AnnData.concatenate(all_shared_ann, all_not_shared_ann, batch_key="concat_batch", index_unique=None)
        if "concat_batch" in all_shared_ann.obs.columns:
            del all_corrected_data.obs["concat_batch"]
        corrected = anndata.AnnData(network.reconstruct(all_corrected_data.X, use_data=True))
        corrected.obs = pd.concat([all_shared_ann.obs, all_not_shared_ann.obs])
        corrected.var_names = adata.var_names.tolist()
        corrected = corrected[adata.obs_names]
        if adata.raw is not None:
            adata_raw = anndata.AnnData(X=adata.raw.X, var=adata.raw.var)
            adata_raw.obs_names = adata.obs_names
            corrected.raw = adata_raw
        corrected.obsm["latent"] = all_corrected_data.X
        return corrected