code: def meta_index_Tensor(self, indices):
      assert indices, "at least one index must be provided"
      # aten::index is the internal advanced indexing implementation
      # checkIndexTensorTypes and expandTensors
      result: List[Optional[torch.Tensor]] = []
      for i, index in enumerate(indices):
          if index is not None:
              assert index.dtype in [
                  torch.long,
                  torch.int8,
                  torch.bool,
              ], "tensors used as indices must be long, byte or bool tensors"
              if index.dtype in [torch.int8, torch.bool]:
                  nonzero = index.nonzero()
                  k = len(result)
                  assert (
                      k + index.ndim <= self.ndim
                  ), f"too many indices for tensor of dimension {self.ndim}"
                  for j in range(index.ndim):
                      assert (
                          index.shape[j] == self.shape[k + j]
                      ), f"The shape of the mask {index.shape} at index {i} does not match the shape of the indexed tensor {self.shape} at index {k + j}"
                      result.append(nonzero.select(1, j))
              else:
                  result.append(index)
          else:
              result.append(index)
      indices = result
      assert (
          len(indices) <= self.ndim
      ), f"too many indices for tensor of dimension {self.ndim} (got {len(indices)})"
      # expand_outplace
      import torch._refs as refs
  
      indices = list(refs._maybe_broadcast(*indices))
      # add missing null tensors
      while len(indices) < self.ndim:
          indices.append(None)
  
      # hasContiguousSubspace
      #   true if all non-null tensors are adjacent
      # See:
      # https://numpy.org/doc/stable/user/basics.indexing.html#combining-advanced-and-basic-indexing
      # https://stackoverflow.com/questions/53841497/why-does-numpy-mixed-basic-advanced-indexing-depend-on-slice-adjacency
      state = 0
      has_contiguous_subspace = False
      for index in indices:
          if state == 0:
              if index is not None:
                  state = 1
          elif state == 1:
              if index is None:
                  state = 2
          else:
              if index is not None:
                  break
      else:
          has_contiguous_subspace = True
  
      # transposeToFront
      # This is the logic that causes the newly inserted dimensions to show up
      # at the beginning of the tensor, if they're not contiguous
      if not has_contiguous_subspace:
          dims = []
          transposed_indices = []
          for i, index in enumerate(indices):
              if index is not None:
                  dims.append(i)
                  transposed_indices.append(index)
          for i, index in enumerate(indices):
              if index is None:
                  dims.append(i)
                  transposed_indices.append(index)
          self = self.permute(dims)
          indices = transposed_indices
  
      # AdvancedIndex::AdvancedIndex
      # Now we can assume the indices have contiguous subspace
      # This is simplified from AdvancedIndex which goes to more effort
      # to put the input and indices in a form so that TensorIterator can
      # take them.  If we write a ref for this, probably that logic should
      # get implemented
      before_shape: List[int] = []
      after_shape: List[int] = []
      replacement_shape: List[int] = []
      for dim, index in enumerate(indices):
          if index is None:
              if replacement_shape:
                  after_shape.append(self.shape[dim])
              else:
                  before_shape.append(self.shape[dim])
          else:
              replacement_shape = list(index.shape)
      return self.new_empty(
          before_shape + replacement_shape + after_shape
      )
  
imports: from typing import List, Optional, Union
  import torch
  from torch.utils._pytree import tree_map
label_desc: Use image node_id 24 for calling a global function with example usage: meta_index_Tensor(indices) and returns: self