code: def _transform_to_backend_dtype_1d(
      data, series_name=None, backend="numpy", dtype=None
  ):
      """
      Supports method-by-method selection of backend type (cupy, cudf, etc.)
      to avoid costly conversion such as row-major to column-major transformation.
      This method is used for 1-dimensional data, and does not perform unncessary
      transpositions or copies.
  
      Note: If using inferred dtypes, the returned series, array, or tensor may
      infer a different dtype than what was originally on the server (i.e promotion
      of int32 to int64).  In the future, the server may also return dtype to prevent
      this from occurring.
  
      data : np.ndarray
          The raw ndarray that will be transformed to the backend dtype.
      series_name : string
          The name of the series (only used for dataframe backends).
      backend : ('numpy', 'pandas', 'cupy', 'cudf', 'torch', 'torch:<device>')
                [default = 'numpy']
      dtype : ('int32', 'int64', 'float32', etc.)
          Optional. The data type to use when storing data in a series or array.
          If not set, it will be inferred for dataframe backends, and assumed as float64
          for array and tensor backends.
  
      """
  
      if backend == "numpy":
          if dtype == data.dtype:
              return data
          else:
              return np.array(data, dtype=dtype or "float64")
      elif backend == "cupy":
          return cupy.array(data, dtype=dtype or "float64")
      elif backend == "pandas":
          return pandas.Series(data, name=series_name, dtype=dtype)
      elif backend == "cudf":
          return cudf.Series(data, name=series_name, dtype=dtype)
      elif backend == "torch":
          return torch.tensor(data.astype(dtype=dtype or "float64"))
  
      backend = backend.split(":")
      if backend[0] == "torch":
          try:
              device = int(backend[1])
          except ValueError:
              device = backend[1]
          return torch.tensor(
              data.astype(dtype=dtype or "float64"), device=device
          )
  
      raise ValueError(f"invalid backend {backend[0]}")
  
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 2 for calling a global function with example usage: _transform_to_backend_dtype_1d(data, series_name, backend, dtype) and returns: torch, data, np, cupy, pandas, cudf, torch