code: def map_index(
      src: Tensor,
      index: Tensor,
      max_index: Optional[Union[int, Tensor]] = None,
      inclusive: bool = False,
  ) -> Tuple[Tensor, Optional[Tensor]]:
      r"""Maps indices in :obj:`src` to the positional value of their
      corresponding occurence in :obj:`index`.
      Indices must be strictly positive.
  
      Args:
          src (torch.Tensor): The source tensor to map.
          index (torch.Tensor): The index tensor that denotes the new mapping.
          max_index (int, optional): The maximum index value.
              (default :obj:`None`)
          inclusive (bool, optional): If set to :obj:`True`, it is assumed that
              every entry in :obj:`src` has a valid entry in :obj:`index`.
              Can speed-up computation. (default: :obj:`False`)
  
      :rtype: (:class:`torch.Tensor`, :class:`torch.BoolTensor`)
  
      Examples:
          >>> src = torch.tensor([2, 0, 1, 0, 3])
          >>> index = torch.tensor([3, 2, 0, 1])
  
          >>> map_index(src, index)
          (tensor([1, 2, 3, 2, 0]), tensor([True, True, True, True, True]))
  
          >>> src = torch.tensor([2, 0, 1, 0, 3])
          >>> index = torch.tensor([3, 2, 0])
  
          >>> map_index(src, index)
          (tensor([1, 2, 2, 0]), tensor([True, True, False, True, True]))
  
      .. note::
  
          If inputs are on GPU and :obj:`cudf` is available, consider using RMM
          for significant speed boosts.
          Proceed with caution as RMM may conflict with other allocators or
          fragments.
  
          .. code-block:: python
  
              import rmm
              rmm.reinitialize(pool_allocator=True)
              torch.cuda.memory.change_current_allocator(rmm.rmm_torch_allocator)
      """
      if src.is_floating_point():
          raise ValueError(
              f"Expected 'src' to be an index (got '{src.dtype}')"
          )
      if index.is_floating_point():
          raise ValueError(
              f"Expected 'index' to be an index (got "
              f"'{index.dtype}')"
          )
      if src.device != index.device:
          raise ValueError(
              f"Both 'src' and 'index' must be on the same device "
              f"(got '{src.device}' and '{index.device}')"
          )
  
      if max_index is None:
          max_index = torch.maximum(src.max(), index.max())
  
      # If the `max_index` is in a reasonable range, we can accelerate this
      # operation by creating a helper vector to perform the mapping.
      # NOTE This will potentially consumes a large chunk of memory
      # (max_index=10 million => ~75MB), so we cap it at a reasonable size:
      THRESHOLD = 40_000_000 if src.is_cuda else 10_000_000
      if max_index <= THRESHOLD:
          if inclusive:
              assoc = src.new_empty(max_index + 1)  # type: ignore
          else:
              assoc = src.new_full((max_index + 1,), -1)  # type: ignore
          assoc[index] = torch.arange(
              index.numel(), dtype=src.dtype, device=src.device
          )
          out = assoc[src]
  
          if inclusive:
              return out, None
          else:
              mask = out != -1
              return out[mask], mask
  
      WITH_CUDF = False
      if src.is_cuda:
          try:
              import cudf
  
              WITH_CUDF = True
          except ImportError:
              import pandas as pd
  
              warnings.warn(
                  "Using CPU-based processing within 'map_index' "
                  "which may cause slowdowns and device "
                  "synchronization. Consider installing 'cudf' to "
                  "accelerate computation"
              )
      else:
          import pandas as pd
  
      if not WITH_CUDF:
          left_ser = pd.Series(src.cpu().numpy(), name="left_ser")
          right_ser = pd.Series(
              index=index.cpu().numpy(),
              data=pd.RangeIndex(0, index.size(0)),
              name="right_ser",
          )
  
          result = pd.merge(
              left_ser,
              right_ser,
              how="left",
              left_on="left_ser",
              right_index=True,
          )
  
          out = torch.from_numpy(result["right_ser"].values).to(
              index.device
          )
  
          if out.is_floating_point() and inclusive:
              raise ValueError(
                  "Found invalid entries in 'src' that do not have "
                  "a corresponding entry in 'index'. Set "
                  "`inclusive=False` to ignore these entries."
              )
  
          if out.is_floating_point():
              mask = torch.isnan(out).logical_not_()
              out = out[mask].to(index.dtype)
              return out, mask
  
          if inclusive:
              return out, None
          else:
              mask = out != -1
              return out[mask], mask
  
      else:
          left_ser = cudf.Series(src, name="left_ser")
          right_ser = cudf.Series(
              index=index,
              data=cudf.RangeIndex(0, index.size(0)),
              name="right_ser",
          )
  
          result = cudf.merge(
              left_ser,
              right_ser,
              how="left",
              left_on="left_ser",
              right_index=True,
              sort=True,
          )
  
          if inclusive:
              try:
                  out = from_dlpack(result["right_ser"].to_dlpack())
              except ValueError:
                  raise ValueError(
                      "Found invalid entries in 'src' that do not "
                      "have a corresponding entry in 'index'. Set "
                      "`inclusive=False` to ignore these entries."
                  )
          else:
              out = from_dlpack(
                  result["right_ser"].fillna(-1).to_dlpack()
              )
  
          out = out[src.argsort().argsort()]  # Restore original order.
  
          if inclusive:
              return out, None
          else:
              mask = out != -1
              return out[mask], mask
  
imports: import warnings
  from typing import Optional, Tuple, Union
  import torch
  from torch import Tensor
  from torch.utils.dlpack import from_dlpack
label_desc: Use image node_id 1 for calling a global function with example usage: map_index(src, index, max_index, inclusive) and returns: out,  None, out,  mask, out,  mask, out,  None, out,  mask, out,  None, out,  mask