RandomBatchGeoSampler produces nan or nodata values about torchgeo HOT 6 CLOSED

import rasterio
from torchgeo.datasets import BoundingBox, RasterDataset

path = "data/05_model_input/pre_processed_multiband.tif"

# Original data does not contain NaNs
f = rasterio.open(path)
ar = f.read()
print(ar)
print("NaNs:", (ar == f.nodata).sum())

# But it does when read by TorchGeo
ds = RasterDataset(path)
query = BoundingBox(558648, 559048, 5184009, 5184409, 0, 1e19)
sample = ds[query]
image = sample["image"]
print(image)
print("NaNs:", (image == f.nodata).sum().item())

import rasterio.merge

path = "data/05_model_input/pre_processed_multiband.tif"
rasterio.merge.merge([path])

def export_to_multiband_geotiff(master_dataset: xarray.Dataset, paramters: dict) -> str:
    model_input_path = Path("/workspaces/avalanche/project/data/05_model_input")
    save_path = model_input_path / f"pre_processed_multiband.tif"

    bands_stack = xarray.concat([master_dataset["vv"], master_dataset["vh"], master_dataset["vvvh"]], dim="band")
    bands_stack = bands_stack.assign_coords(band=("band", ["vv", "vh", "vvvh"]))

    LOG.info(f"Saving multiband data to {save_path}")
    bands_stack = bands_stack.rio.write_crs(master_dataset.rio.crs)

    nodata_value = paramters["master_options"]["geotiff_nodata"]
    LOG.info(f"Setting nodata value to {nodata_value}")
    bands_stack.rio.write_nodata(nodata_value, inplace=True)
    bands_stack.rio.to_raster(save_path)

    return save_path.as_posix()