How to use the graphviz.Source function in graphviz

import graphviz
import pandas
from sklearn import tree

clf = tree.DecisionTreeClassifier()
input = pandas.read_csv("/home/glenn/git/clojure-news-feed/client/ml/etl/throughput.csv")
data = input[input.columns[6:9]]
target = input['cloud']
clf = clf.fit(data, target)
dot_data = tree.export_graphviz(clf, out_file=None)
graph = graphviz.Source(dot_data) 
graph.render(view=True)

import glob
    import tempfile
    from graphviz import Source
    from PIL import Image, ImageDraw, ImageFont
    with open(filename) as dot, tempfile.TemporaryDirectory() as tempDirectory:
        content = dot.read()
        # find out subworkflows
        subworkflows = [
            line.split()[2].strip('"')
            for line in content.splitlines()
            if line.startswith('strict digraph')
        ]
        # find out unique subgraphs
        unique_subworkflows = list(dict.fromkeys(subworkflows))
        #
        src = Source(content)
        src.format = 'png'
        outfile = src.render(filename='sosDot', directory=tempDirectory)
        # dot command can generate more than outfiles returned by the render function
        pngFiles = glob.glob(os.path.join(tempDirectory, f'sosDot*.png'))
        if len(pngFiles) == 1:
            return b64_of(outfile)
        # create a gif files from multiple png files
        pngFiles.sort(
            key=lambda x: int(os.path.basename(x)[:-3].split('.')[1] or 0))
        # find maximum size for all graphs corresponding to their subgraphs
        maxWidth = 0
        wf_maxHeight = {}
        images = {}
        for subworkflow in unique_subworkflows:
            wf_images = {
                png: Image.open(png)

# Convert to png and show full picture
            filename = log_path + '/monitor/' + name
            extension = 'png'
            import os
            if os.path.exists(filename + '.' + extension):
                os.remove(filename + '.' + extension)
            s = Source(text, filename=filename, format=extension)
            s.render()
            from IPython.display import Image
            image = Image(filename=filename + '.' + extension)
            return image
        except Exception:
            print('Oops! Failed rendering the graph.')
            raise
    else:
        return Source(text)

This is required for new converter path, which maintains and propagates shapes while converting operators.
    """
    if not custom_conversion_functions:
        custom_conversion_functions = dict()
    if not custom_shape_functions:
        custom_shape_functions = dict()
    if not optional_inputs:
        optional_inputs = list()

    if outputs is not None:
        ssa.extract_subgraph(outputs, name=top_func)

    if DEBUG:
        import graphviz
        dot_string = ssa.get_dot_string(annotation=True, name_and_op_style=False, highlight_debug_nodes=[])
        graphviz.Source(dot_string).view(filename='/tmp/ssa')

    # apply passes on the ssa, prior to conversion
    passes = [
        constant_weight_link_removal,
        fuse_bias_add,
        onehot_matmul_to_embedding,
        fuse_layer_norm,
        fuse_gelu,
        transform_nhwc_to_nchw,
        remove_identity,
        remove_no_ops_and_shift_control_dependencies,
        remove_single_isolated_node,
        fuse_conv_mul_add_into_batchnorm,
        spatial_reduce_to_global_pool,
        fuse_pad_into_conv,
        remove_oneway_split,

def tree_legend():
    """Display a legend for the colors of the tree method"""
    import graphviz
    return graphviz.Source(tree_legend_dot())

def export_tree(clf, feature_names, name):
    dot_data = tree.export_graphviz(clf, out_file=None,
                                    feature_names=feature_names,
                                    class_names=clf.classes_,
                                    filled=True, rounded=True,
                                    special_characters=True)

    graph = graphviz.Source(dot_data)
    graph.render(name, cleanup=True)

def visualize_graph(graph: WillumpGraph, mi_inputs: List[WillumpGraphNode] = (),
                    li_inputs: List[WillumpGraphNode] = (),
                    indices_to_costs_map: Mapping = None,
                    indices_to_importances_map: Mapping = None, model_node_inputs=None) -> None:
    labels_string, graph_string = graph_to_dot_string(graph, mi_inputs, li_inputs,
                                                      indices_to_costs_map, indices_to_importances_map,
                                                      model_node_inputs)
    dot_string = "digraph G {{\n {0} {1}}}".format(labels_string, graph_string)
    print(dot_string)
    source = graphviz.Source(dot_string)
    if "microsoft" in platform.uname()[3].lower():  # Detect WSL
        source.render(format="png", filename="output")
        subprocess.run(["cmd.exe", "/C", "call", "output.png"])
    else:
        source.render(format="png", filename="output", view=True)

print(confusion_matrix)
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))
#%%% 
y_test.shape, y_pred.shape
y_test.head()
y_pred[0:6]
#%%%
from graphviz import Source
from sklearn import tree
from IPython.display import SVG
#libraries & path of graphviz
import os
os.environ["PATH"] += os.pathsep + 'c:/Program Files (x86)/Graphviz2.38/bin/'
#%%
graph1 = Source(tree.export_graphviz(clf, out_file=None, class_names= ['0', '1']  , filled = True))
display(SVG(graph1.pipe(format='svg')))
#change labels names
graph2 = Source( tree.export_graphviz(clf, out_file=None, feature_names=X.columns, filled=True, class_names=['NoDiabetis','Diabetis']))
graph2
#change max_depth : 1 to 4
Source(tree.export_graphviz(clf, out_file=None, max_depth=1, feature_names=X.columns, class_names=['NonDB','DB'], label='all', filled=True, leaves_parallel=True, impurity=True, node_ids=True, proportion=True, rotate=True, rounded=True, special_characters=False, precision=1))
#https://stackoverflow.com/questions/27817994/visualizing-decision-tree-in-scikit-learn
# This is for saving image in file system
#https://scikit-learn.org/stable/modules/generated/sklearn.tree.export_graphviz.html

#visualise using dotfile

#True should be returned. goto location and see the file

#%%%  Create Decision Tree classifer object
#change max_depth at the time of creation and method