Could not deploy: Expected <class 'edgeimpulse.model.output_type.Regression'> model to have 2 output dimensions but has 3

Question/Issue: Cannot generate TinyML model via using python-sdk.

Project ID: 246447

Context/Use case: I am predicting IMU data via 1D CNN-based model. My board is Nicla Sense ME. The model architecture as follows:

Model: "sequential_19"
 Layer (type)                Output Shape              Param #   
 conv1d_68 (Conv1D)          (None, 41, 8)             368       
 max_pooling1d_38 (MaxPoolin  (None, 13, 8)            0         
 flatten_19 (Flatten)        (None, 104)               0         
 dense_19 (Dense)            (None, 9)                 945       
 reshape_17 (Reshape)        (None, 1, 9)              0         
Total params: 1,313
Trainable params: 1,313
Non-trainable params: 0

When I try to generate my model via:

# Set the output name.
deploy_filename = ""

# Set model information, such as your list of labels
model_output_type = ei.model.output_type.Regression()

# Create C++ library with trained model.
# Initialize the tiny_model.
deploy_bytes = None
    deploy_bytes = ei.model.deploy(model=model,
except Exception as e:
    print(f"Could not deploy: {e}")
# Write the downloaded raw bytes to a file
if deploy_bytes:
    with open(deploy_filename, 'wb') as f:

I am getting the following error:
Could not deploy: Expected <class 'edgeimpulse.model.output_type.Regression'> model to have 2 output dimensions but has 3

My guess is, maybe it is due to timestep [32, 1, 9]? But I believe I need to have that there.
Also is python-sdk open source? I could only find the docs here:

Many thanks.

If I remove the timestep [32, 1, 9] from output, I get the following error:
Could not deploy: Expected Regression model to have scalar output but has vector with length 9

Seems like regression can only be done with 1 output feature. In my case, I have accelerometer, gyroscope, and magnetometer data (all of them are 3-axes) so 9 features to predict.

How/Can we implement such a thing?