Earth Engine client library not initialized when using a dask cluster and xarray?

Question

I'm trying to test running a simple math operation client-side using xee, an xarray extension for the Google Earth Engine API. When I set up the Dask client and run the compute() method I keep getting

EEException: Earth Engine client library not initialized. Run ee.Initialize().

I do run the ee.Initialize() function after I set up the Dask client, but I'm assuming this function is bring ran outside the scope of the workers in my Dask cluster. So how do I run ee.Initialize() within the scope of the cluster? I assumed that once I set up the cluster in my Jupyter notebook that any code run after the set up is run within the workers.

import ee
import xarray

from dask.distributed import Client
client = Client(n_workers=2, threads_per_worker=2, memory_limit='1GB')
client

ee.Initialize(opt_url='https://earthengine-highvolume.googleapis.com')

def prep_sr_l8(image):
    # Develop masks for unwanted pixels (fill, cloud, cloud shadow).
    qa_mask = image.select('QA_PIXEL').bitwiseAnd(int('11111', 2)).eq(0)
    saturation_mask = image.select('QA_RADSAT').eq(0)

    # Apply the scaling factors to the appropriate bands.
    def get_factor_img(factor_names):
        factor_list = image.toDictionary().select(factor_names).values()
        return ee.Image.constant(factor_list)
    
    scale_img = get_factor_img([
        'REFLECTANCE_MULT_BAND_.|TEMPERATURE_MULT_BAND_ST_B10'])
    offset_img = get_factor_img([
        'REFLECTANCE_ADD_BAND_.|TEMPERATURE_ADD_BAND_ST_B10'])
    scaled = image.select('SR_B.|ST_B10').multiply(scale_img).add(offset_img)

    # Replace original bands with scaled bands and apply masks.
    return image.addBands(scaled, None, True)\
        .updateMask(qa_mask).updateMask(saturation_mask)

LTBMU = ee.FeatureCollection("projects/calfuels/assets/Boundaries/LTBMU_remove_NV_remove_lake")

ic = (ee.ImageCollection('LANDSAT/LC08/C02/T1_L2').map(prep_sr_l8).filterBounds(LTBMU.geometry())
      .filterDate('2019-01-01', '2019-12-31'))

ic_xr = xarray.open_dataset(ic, engine = "ee", crs='EPSG:3310', scale = 30, chunks="auto")

ndvi = (ic_xr['SR_B5'] - ic_xr['SR_B4']) / (ic_xr['SR_B5'] - ic_xr['SR_B4'])
ic_xr['NDVI'] = ndvi

ic_xr_result = ic_xr.compute()