class_docstr: Storage for node/edge feature data.
code: def __init__(
      self,
      pg,
      column,
      storage_type,
      backend_lib="torch",
      indices_offset=0,
      types_to_fetch=None,
  ):
      self.pg = pg
      self.column = column
  
      if backend_lib == "torch":
          from torch.utils.dlpack import from_dlpack
      elif backend_lib == "tf":
          from tensorflow.experimental.dlpack import from_dlpack
      elif backend_lib == "cupy":
          from cupy import from_dlpack
      elif backend_lib == "numpy":
          pass
      else:
          raise NotImplementedError(
              f"Only PyTorch ('torch'), TensorFlow ('tf'), and CuPy ('cupy')"
              f"and numpy ('numpy') backends are currently supported, "
              f" got {backend_lib=}"
          )
      if storage_type not in ["edge", "node"]:
          raise NotImplementedError(
              "Only edge and node storage is supported"
          )
  
      self.storage_type = storage_type
  
      self.from_dlpack = from_dlpack
      self.indices_offset = indices_offset
      self.types_to_fetch = types_to_fetch
  
imports: import numpy
label_desc: Use image node_id 1 to create a new CuFeatureStorage object  with example: obj = CuFeatureStorage(pg, column, storage_type, backend_lib, indices_offset, types_to_fetch)