How to use the scanpy.pl function in scanpy

sc.pp.neighbors(latent_with_true_labels)
        sc.tl.umap(latent_with_true_labels)
        sc.pl.umap(latent_with_true_labels, color=color,
                   save=f"_{data_name}_{cell_type}_latent_with_true_labels",
                   show=False)

        sc.pp.neighbors(latent_with_fake_labels)
        sc.tl.umap(latent_with_fake_labels)
        sc.pl.umap(latent_with_fake_labels, color=color,
                   save=f"_{data_name}_{cell_type}_latent_with_fake_labels",
                   show=False)

        sc.pp.neighbors(mmd_latent_with_true_labels)
        sc.tl.umap(mmd_latent_with_true_labels)
        sc.pl.umap(mmd_latent_with_true_labels, color=color,
                   save=f"_{data_name}_{cell_type}_mmd_latent_with_true_labels",
                   show=False)

        sc.pp.neighbors(mmd_latent_with_fake_labels)
        sc.tl.umap(mmd_latent_with_fake_labels)
        sc.pl.umap(mmd_latent_with_fake_labels, color=color,
                   save=f"_{data_name}_{cell_type}_mmd_latent_with_fake_labels",
                   show=False)

        sc.pl.violin(cell_type_adata, keys=top_100_genes[0], groupby='condition',
                     save=f"_{data_name}_{cell_type}_{top_100_genes[0]}",
                     show=False)

        plt.close("all")

_, rec, mmd = network.get_reconstruction_error(net_valid_adata, condition_key)

    row = [alpha, eta, z_dim, mmd_dim, beta, asw, nmi, ari, ebm, rec, mmd]
    with open(f"./{filename}.csv", 'a') as file:
        writer = csv.writer(file)
        writer.writerow(row)
    file.close()

    os.makedirs(f"./results/Monitor/{filename}/", exist_ok=True)
    sc.settings.figdir = f"./results/Monitor/{filename}/"

    sc.pp.neighbors(mmd_latent)
    sc.tl.umap(mmd_latent)
    sc.pl.umap(mmd_latent, color=condition_key, frameon=False, title="", save=f"_trVAE_MMD_condition_{beta}.pdf")
    sc.pl.umap(mmd_latent, color=cell_type_key, frameon=False, title="", save=f"_trVAE_MMD_cell_type_{beta}.pdf")

    K.clear_session()

mmd_latent_with_fake_labels = sc.AnnData(X=mmd_latent_with_fake_labels)
        mmd_latent_with_fake_labels.obs['condition'] = data.obs['condition'].values
        mmd_latent_with_fake_labels.obs[cell_type_key] = data.obs[cell_type_key].values

        color = ['condition', cell_type_key]

        sc.pp.neighbors(train_data)
        sc.tl.umap(train_data)
        sc.pl.umap(train_data, color=color,
                   save=f'_{data_name}_{cell_type}_train_data',
                   show=False)

        sc.pp.neighbors(latent_with_true_labels)
        sc.tl.umap(latent_with_true_labels)
        sc.pl.umap(latent_with_true_labels, color=color,
                   save=f"_{data_name}_{cell_type}_latent_with_true_labels",
                   show=False)

        sc.pp.neighbors(latent_with_fake_labels)
        sc.tl.umap(latent_with_fake_labels)
        sc.pl.umap(latent_with_fake_labels, color=color,
                   save=f"_{data_name}_{cell_type}_latent_with_fake_labels",
                   show=False)

        sc.pp.neighbors(mmd_latent_with_true_labels)
        sc.tl.umap(mmd_latent_with_true_labels)
        sc.pl.umap(mmd_latent_with_true_labels, color=color,
                   save=f"_{data_name}_{cell_type}_mmd_latent_with_true_labels",
                   show=False)

        sc.pp.neighbors(mmd_latent_with_fake_labels)

train = sc.read("../data/chsal_train_7000.h5ad")
    elif data_name == "species":
        stim_key = "LPS6"
        ctrl_key = "unst"
        cell_type_key = "species"
        train = sc.read("../data/train_all_lps6.h5ad")
    conditions = {"ctrl": ctrl_key, "stim": stim_key}

    os.chdir(f"./vae_results/{data_name}/")
    sc.settings.figdir = os.getcwd()

    recon_data = sc.read(f"./reconstructed.h5ad")
    diff_genes = score(train, n_deg=500, n_genes=1000, cell_type_key=cell_type_key, conditions=conditions,
                       sortby=score_type)
    diff_genes = diff_genes["genes"].tolist()
    sc.pl.stacked_violin(recon_data,
                         var_names=diff_genes[:10],
                         groupby="condition",
                         save=f"_Top_{10}_{score_type}_genes_out_of_500_{data_name}",
                         swap_axes=True,
                         show=False)
    os.chdir("../../")

wspace=0.15,
                   frameon=False)

        sc.pp.neighbors(mmd_latent_with_true_labels)
        sc.tl.umap(mmd_latent_with_true_labels)
        sc.pl.umap(mmd_latent_with_true_labels, color=color,
                   save=f"_{data_name}_{cell_type}_mmd_latent_with_true_labels",
                   show=False,
                   wspace=0.15,
                   frameon=False)
        if gene_list is not None:
            for target_condition in target_keys:
                pred_adata = pred_adatas[pred_adatas.obs[condition_key].str.endswith(target_condition)]
                violin_adata = cell_type_adata.concatenate(pred_adata)
                for gene in gene_list[:3]:
                    sc.pl.violin(violin_adata, keys=gene, groupby=condition_key,
                                 save=f"_{data_name}_{cell_type}_{gene}_{target_condition}.pdf",
                                 show=False,
                                 wspace=0.2,
                                 rotation=90,
                                 frameon=False)

        plt.close("all")

model_path=f"./")

        # network.restore_model()
        network.train(net_train_data, n_epochs=n_epochs, batch_size=batch_size, verbose=2)
        print(f"network_{cell_type} has been trained!")

        true_labels, _ = scgen.label_encoder(net_train_data)
        fake_labels = np.ones(shape=(net_train_data.shape[0], 1))

        latent_with_true_labels = network.to_latent(net_train_data.X, labels=true_labels)
        latent_with_true_labels = sc.AnnData(X=latent_with_true_labels,
                                             obs={condition_key: net_train_data.obs[condition_key].tolist(),
                                                  cell_type_key: net_train_data.obs[cell_type_key].tolist()})
        sc.pp.neighbors(latent_with_true_labels)
        sc.tl.umap(latent_with_true_labels)
        sc.pl.umap(latent_with_true_labels, color=[condition_key, cell_type_key],
                   save=f"_latent_true_labels_{z_dim}",
                   show=False)

        latent_with_fake_labels = network.to_latent(net_train_data.X, fake_labels)
        latent_with_fake_labels = sc.AnnData(X=latent_with_fake_labels,
                                             obs={condition_key: net_train_data.obs[condition_key].tolist(),
                                                  cell_type_key: net_train_data.obs[cell_type_key].tolist()})
        sc.pp.neighbors(latent_with_fake_labels)
        sc.tl.umap(latent_with_fake_labels)
        sc.pl.umap(latent_with_fake_labels, color=[condition_key, cell_type_key],
                   save=f"_latent_fake_labels_{z_dim}",
                   show=False)

        mmd_with_true_labels = network.to_mmd_layer(network.cvae_model, net_train_data.X,
                                                    encoder_labels=true_labels, feed_fake=False)
        mmd_with_true_labels = sc.AnnData(X=mmd_with_true_labels,

mmd_latent_with_fake_labels = sc.AnnData(X=mmd_latent_with_fake_labels)
    mmd_latent_with_fake_labels.obs['condition'] = data.obs['condition'].values
    # mmd_latent_with_fake_labels.obs[cell_type_key] = data.obs[cell_type_key].values

    color = ['condition']

    sc.pp.neighbors(data)
    sc.tl.umap(data)
    sc.pl.umap(data, color=color,
               save=f'_{data_name}_train_data',
               show=False)

    sc.pp.neighbors(latent_with_true_labels)
    sc.tl.umap(latent_with_true_labels)
    sc.pl.umap(latent_with_true_labels, color=color,
               save=f"_{data_name}_latent_with_true_labels",
               show=False)

    sc.pp.neighbors(latent_with_fake_labels)
    sc.tl.umap(latent_with_fake_labels)
    sc.pl.umap(latent_with_fake_labels, color=color,
               save=f"_{data_name}__latent_with_fake_labels",
               show=False)

    sc.pp.neighbors(mmd_latent_with_true_labels)
    sc.tl.umap(mmd_latent_with_true_labels)
    sc.pl.umap(mmd_latent_with_true_labels, color=color,
               save=f"_{data_name}_mmd_latent_with_true_labels",
               show=False)

    sc.pp.neighbors(mmd_latent_with_fake_labels)

def run_embedding(adata):
        if len(unique(adata.obs["louvain"].values)) < 10:
            palette = "tab10"
        else:
            palette = "tab20"

        if "umap" in embedding:
            sc.tl.umap(adata)
            if plotting:
                sc.pl.umap(adata, color="louvain", palette=palette, save="_louvain")

        if "tsne" in embedding:
            sc.tl.tsne(adata)
            if plotting:
                sc.pl.tsne(adata, color="louvain", palette=palette, save="_louvain")

save="pred_all_genes",
                           show=False)

                sc.pp.neighbors(all_adata_top_100_genes)
                sc.tl.umap(all_adata_top_100_genes)
                sc.pl.umap(all_adata_top_100_genes, color=condition_key,
                           save="pred_top_100_genes",
                           show=False)

                sc.pp.neighbors(all_adata_top_50_genes)
                sc.tl.umap(all_adata_top_50_genes)
                sc.pl.umap(all_adata_top_50_genes, color=condition_key,
                           save="pred_top_50_genes",
                           show=False)

        sc.pl.violin(all_adata, keys=diff_genes.tolist()[0], groupby=condition_key,
                     save=f"_{diff_genes.tolist()[0]}",
                     show=False)

        plt.close("all")

save=None):
	"""\
    Scatter plot in tSNE basis.

    Parameters
    ----------
    {adata_color_etc}
    {edges_arrows}
    {scatter_bulk}
    {show_save_ax}

    Returns
    -------
    If `show==False` a :class:`~matplotlib.axes.Axes` or a list of it.
    """
	sc.pl.tsne(adata, color=color, gene_symbols=feature_symbols, use_raw=use_raw,
        layer=layer, sort_order=sort_order, groups=groups, components=components,
        projection=projection, legend_loc=legend_loc, legend_fontsize=legend_fontsize,
        legend_fontweight=legend_fontweight, legend_fontoutline=legend_fontoutline,
        size=size, color_map=color_map, palette=palette, frameon=frameon, ncols=ncols,
        wspace=wspace, hspace=hspace, title=title, return_fig=return_fig, show=show,
        save=save)