EM_PREDICTION = prediction[0][0] TypeError: 'float' object is not subscribable

Describe the issue
We are trying to register the model with pzmm sasctl (1.10.1) and MAS in Viya 3.5. When we try to score, it fails with the error: "EM_PREDICTION = prediction[0][0] TypeError: 'float' object is not subscribable" Error: pm.execute failed. resultCode= -1958744061
So far, we found out that if the input data shape =1, then the result must be a scalar value prediction[0][0]. Our model returns an array of floats and fails because of this.
Is it possible to have a model input as a row but the output as a table?

To Reproduce
Attaching notebook and SAS MM log file from GUI score button.
MAS_PYMAS_ERROR_FLOAT.txt
register_model.txt

Expected behavior
When I use a single row to score as a dictionary using the below code it should return a predicted value:-
from sasctl.services import microanalytic_score as mas
score_obj = dict(data_to_score.iloc[0,:][slump_col])
mas_obj = mas.execute_module_step("slump0min", "score", **score_obj)
for key, value in mas_obj.items():
print(f"{key}: {value}")

resultCode: -1958743884.0
EM_PREDICTION: None

When I use the score GUI from SAS MM using a data table stored in PUBLIC lib with more than one row, it should give me the scored regression output from catboost model

Stack Trace
ERROR: Line 301: Traceback (most recent call last):
File "/opt/sas/viya/home/SASFoundation/misc/embscoreeng/mas2py.py", line 942, in invoke
out = up[1].get(func)0(*args)
File "/tmp/tmprluhpq0f/model_exec_932ed736-60cf-4a85-8edf-4097d9741c44.py", line 55, in score
EM_PREDICTION = prediction[0][0] TypeError: 'float' object is not subscribable

Version
sasctl (1.10.1)

Beginnen Sie mit den angehängten MAS_PYMAS_ERROR_FLOAT.txt- und register_model.txt-Logs und verfolgen Sie anschließend den Einstiegspunkt mas.execute_module_step("slump0min", "score", ...) sowie die im Stacktrace angezeigte generierte score-Funktion. Bestimmen Sie den erwarteten Ausgabe-Vertrag für einen Skalar oder ein Array; die Aufgabe ist abgeschlossen, wenn das Scoring für einzelne und mehrere Zeilen den vorhergesagten Wert ohne TypeError zurückgibt.