code: def bert_position_ids(token_ids):
      # Create position ids
      seq_length = token_ids.size(1)
      position_ids = torch.arange(
          seq_length, dtype=torch.long, device=token_ids.device
      )
      position_ids = position_ids.unsqueeze(0).expand_as(token_ids)
  
      return position_ids
  
imports: import contextlib
  import torch
  from apex.transformer import tensor_parallel
  from apex.transformer.enums import AttnMaskType
  from apex.transformer.enums import ModelType
  from apex.transformer.layers import FusedLayerNorm
  from apex.transformer.testing.global_vars import get_args
  from apex.transformer.testing.standalone_transformer_lm import MegatronModule, get_language_model, get_linear_layer, init_method_normal, scaled_init_method_normal, parallel_lm_logits
label_desc: Use image node_id 2 for calling a global function with example usage: bert_position_ids(token_ids) and returns: position_ids