code: def __init__(
      self,
      num_positions: int,
      embedding_dim: int,
      padding_idx: Optional[int] = None,
  ):
      super().__init__(num_positions, embedding_dim)
  
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 1 to create a new WhisperPositionalEmbedding object from inherited base classes: nn with example: obj = WhisperPositionalEmbedding(num_positions, embedding_dim, padding_idx)