code: def forward(self, input_ids, past_key_values_length=0):
      return self.weight[
          past_key_values_length : past_key_values_length
          + input_ids.shape[-1]
      ]
  
imports: import math
  import random
  from typing import Optional, Tuple
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_whisper import WhisperConfig
label_desc: Use image node_id 2 for calling the WhisperPositionalEmbedding obj's underlying member method code with example usage: obj.forward(input_ids, past_key_values_length) and returns: self