code: def cycle_index(num, shift):
      """
      Creates a 1-dimensional tensor of integers with a specified length (`num`) and a cyclic shift (`shift`). The tensor starts with integers from `shift` to `num - 1`, and then wraps around to include integers from `0` to `shift - 1` at the end.
  
      Parameters
      ----------
      num: int
          Length of the tensor.
      shift: int
          Amount to shift the tensor by.
  
      Example
      -------
      >>> num = 10
      >>> shift = 3
      >>> arr = cycle_index(num, shift)
      >>> print(arr)
      tensor([3, 4, 5, 6, 7, 8, 9, 0, 1, 2])
      """
      arr = torch.arange(num) + shift
      arr[-shift:] = torch.arange(shift)
      return arr
  
imports: import random
  import copy
  import torch
  import numpy
  from torch_geometric.nn import GINEConv, GCNConv, GATConv, SAGEConv, global_add_pool, global_mean_pool, global_max_pool
  from torch_geometric.nn.aggr import AttentionalAggregation, Set2Set
  from torch_geometric.nn.inits import uniform
  import torch.nn
  from torch.functional import F
  from deepchem.data import Dataset
  from deepchem.models.losses import SoftmaxCrossEntropy, EdgePredictionLoss, GraphNodeMaskingLoss, GraphEdgeMaskingLoss, DeepGraphInfomaxLoss, GraphContextPredLoss
  from deepchem.models.torch_models import ModularTorchModel
  from deepchem.feat.graph_data import BatchGraphData, GraphData, shortest_path_length
  from typing import Iterable, List, Tuple
  from deepchem.metrics import to_one_hot
label_desc: Use image node_id 4 for calling a global function with example usage: cycle_index(num, shift) and returns: arr