code: def _offsets_to_backend_dtype(offsets, backend):
      """
      Transforms the offsets object into an appropriate object for the given backend.
  
      Parameters
      ----------
      offsets : cugraph_service_client.types.Offsets
          The offsets object to transform.
      backend : ('numpy', 'pandas', 'cupy', 'cudf', 'torch', 'torch:<device>')
                [default = 'numpy']
          The backend the offsets will be transformed to a type of.
  
      Returns
      -------
      An object of the desired backend.
      For cudf: A cudf DataFrame with index=type, start, stop columns
      For pandas: A pandas DataFrame with index=type, start, stop columns
      For cupy: A dict of {'type': np.ndarray, 'start': cp.ndarray, 'stop': cp.ndarray}
      For numpy: A dict of {'type': np.ndarray, 'start': np.ndarray, 'stop': np.ndarray}
      For torch: A dict of {'type': np.ndarray, 'start': Tensor, 'stop': Tensor}
      """
      if backend == "cudf" or backend == "pandas":
          df_clx = (
              cudf.DataFrame if backend == "cudf" else pandas.DataFrame
          )
          df = df_clx(
              {
                  "start": offsets.start,
                  "stop": offsets.stop,
              },
              index=offsets.type,
          )
          df.index.name = "type"
          return df
  
      if backend == "cupy":
          tn_clx = cupy.array
          device = None
      elif backend == "numpy":
          tn_clx = np.array
          device = None
      elif backend == "torch":
          tn_clx = torch.tensor
          device = "cpu"
      else:
          if "torch" not in backend:
              raise ValueError(f"Invalid backend {backend}")
          tn_clx = torch.tensor
          backend = backend.split(":")
          try:
              device = int(backend[1])
          except ValueError:
              device = backend[1]
  
      return_dict = {
          "type": np.array(offsets.type),
          "start": tn_clx(offsets.start),
          "stop": tn_clx(offsets.stop),
      }
  
      if device is not None:
          return_dict["start"] = return_dict["start"].to(device)
          return_dict["stop"] = return_dict["stop"].to(device)
  
      return return_dict
  
file_path: cugraph/python/cugraph-service/client/cugraph_service_client/remote_graph_utils.py
imports: import importlib
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: _offsets_to_backend_dtype(offsets, backend) and returns: return_dict, df