Issue with XGBoost loading a pretrained model in a custom raster function

Hello,

I'm looking for help trouble shooting a custom raster function. The heart of the rf is an XGBoost model, but something goes wrong when I try to load the model. I've used Pickel to help narrow down the issue, and verified that the xgboost library IS loading and that the filepath for the model is correct. For whatever reason, the model is not loading. Using a notebook in ArcPro, I could not replicate the issue (everything runs as it should). It seems to be an issue specifically with the raster function. I'm really at a loss as to how to fix this and any insight would be much appreciated!

Other notes: Everything up to the loading of the model is working (verified this using Pickel). The rf on a whole runs, but provides no output (because the model is not loading).

The code around the issue:

```
def updatePixels(self, tlc, shape, props, **pixelBlocks):
# Preprocessing: Create mask(s)(vector and raster) as needed

# Get bands
image_array = np.array(pixelBlocks['NAIP_ras_pixels'], 'float32')
red = image_array[0,:,:]
green = image_array[1,:,:]
blue = image_array[2,:,:]
nir = image_array[3,:,:]

def calc_rdvi(nir_values, red_values):
nom = nir_values - red_values
denom = np.sqrt(nir_values + red_values)
return (nom/denom)

def calc_osavi(nir_values, red_values):
nom = nir_values - red_values
denom = nir_values + red_values + 0.16
return (nom/denom)

def calc_evi(nir_values, red_values, blue_values, G=2.5, C1=6, C2=7.5, L=1):
nom = nir_values + red_values
denom = nir_values + (C1 * red_values) - (C2 * (blue_values + L))
evi = G*(nom/denom)
evi = np.nan_to_num(evi, posinf=0, neginf=0)
return evi

def calc_vari(green_values, red_values, blue_values):
return (green_values - red_values)/(green_values + red_values - blue_values)

def calc_msavi(nir_values, red_values):
term1a = np.square((2*nir_values) + 1)
term1b = 8*(nir_values-red_values)
term1 = np.sqrt(term1a-term1b)
term2a = (2*nir_values)+1
term2 = term2a - term1
return term2/2

def calc_gli(green_values, red_values, blue_values):
nom = (green_values - red_values) + (green_values - blue_values)
denom = (2*green_values) + red_values + blue_values
return (nom/denom)

rdvi = calc_rdvi(nir, red)
osavi = calc_osavi(nir, red)
evi = calc_evi(nir, red, blue)
vari = calc_vari(green, red, blue)
msavi = calc_msavi(nir, red)
gli = calc_gli(green, red, blue)

array_shape = rdvi.shape

vi_array = np.array([rdvi.flatten(), osavi.flatten(), evi.flatten(), vari.flatten(), msavi.flatten(), gli.flatten()])

del (red, green, blue, nir, rdvi, osavi, evi, vari, msavi, gli)

# Fix any nan or inf that result from VI calculation (problems in nodata areas)
vi_array = np.nan_to_num(vi_array, posinf=0, neginf=0)

# Step 2: Create the dataframe
def generate_vi_df(two_dim_vi_stack):
# create a DF to match XGBoost model
df = pd.DataFrame(columns=['ID','RDVI','OSAVI',
'EVI','VARI','MSAVI','GLI',
], dtype='float32')

df['ID'] = range(0, two_dim_vi_stack.shape[1])
df['RDVI'] = two_dim_vi_stack[0,:]
df['OSAVI'] = two_dim_vi_stack[1,:]
df['EVI'] = two_dim_vi_stack[2,:]
df['VARI'] = two_dim_vi_stack[3,:]
df['MSAVI'] = two_dim_vi_stack[4,:]
df['GLI'] = two_dim_vi_stack[5,:]
return df

df = generate_vi_df(vi_array)

# Step 3: Load the XGBoost model

# Able to output in a Pickel a dict with configs (xgb.get_config()). So XGBoost *is* being imported. 22 Nov 2023.
# test = xgb.get_config()

#(The JSON will load independently, so the path is correct. 22 Nov 2023.)
model_fn = 'TreeCAP_Full_US_NoOrgSpecBandsOREco_RDVI_OSAVI_EVI_VARI_MSAVI_GLI.json'
model_filename = os.path.join(xgb_model_directory, model_fn)

## Unclear if model is actually loading. 22 Nov 2023
bst = xgb.Booster(model_file=model_filename)

## This should provide a dict output (this works in and Arcpro notebook) to test if model is loading,
## but Pickle fails to save output. No file is created at all. 22 Nov 2023
output = bst.get_fscore()

########## save a pickle ##########
fname = 'pixelBlocks.p'
pickle_filename = os.path.join(debug_logs_directory, fname)
pickle.dump(output, open(pickle_filename,"wb"))
########## pickle saved ##########
```