code: def shift_kjt_by_one(x: KeyedJaggedTensor):
      """
      Shift the data by one for each key in a KeyedJaggedTensor
      The last data will then always have no value
  
      Example:
      Input KeyedJaggedTensor (x):
          x = KeyedJaggedTensor(
              keys=["Key0", "Key1"],
              values=[V0, V1, V2, V3, V4, V5, V6]
              lengths=[2, 0, 1, 1, 1, 2]
          )
      which represents data:
                     data0       data1      data2
          "Key0"    [V0,V1]       None       [V2]
          "Key1"      [V3]        [V4]      [V5,V6]
  
      If we wish to shift data by one, then this function will return
                     data0       data1      data2
          "Key0"     None         [V2]       None
          "Key1"     [V4]        [V5,V6]     None
      """
      num_keys = len(x.keys())
      acc_lengths_per_key = torch.cumsum(
          torch.tensor(x.length_per_key()), dim=0
      )
      values_per_key = torch.tensor_split(
          x.values(), acc_lengths_per_key
      )[:-1]
      val_lens_per_key = torch.chunk(x.lengths(), num_keys)
  
      # Shift values, lengths, and weights *WITHIN each key*
      shifted_vals = torch.cat(
          [x[y[0] :] for x, y in zip(values_per_key, val_lens_per_key)]
      )
      shifted_lengths = torch.cat(
          [
              torch.cat([x[1:], torch.tensor([0], device=x.device)])
              for x in val_lens_per_key
          ]
      )
      if x.weights_or_none() is not None:
          weights_per_key = torch.tensor_split(
              x.weights(), acc_lengths_per_key
          )[:-1]
          shifted_weights = torch.cat(
              [
                  x[y[0] :]
                  for x, y in zip(weights_per_key, val_lens_per_key)
              ]
          )
      else:
          shifted_weights = None
  
      res = KeyedJaggedTensor(
          keys=x.keys(),
          lengths=shifted_lengths,
          values=shifted_vals,
          weights=shifted_weights,
      )
      return res
  
file_path: ReAgent/reagent/core/torch_utils.py
imports: from io import BytesIO
  from typing import Dict, List
  import numpy
  import torch
  from torchrec.sparse.jagged_tensor import JaggedTensor, KeyedJaggedTensor
label_desc: Use image node_id 11 for calling a global function with example usage: shift_kjt_by_one(x) and returns: res