class_docstr: This module learns positional embeddings up to a fixed maximum size.
class_docstr_tok: ["This","module","learns","positional","embeddings","up","to","a","fixed","maximum","size","."]
code: def __init__(self, num_embeddings: int, embedding_dim: int):
      # OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
      # and adjust num_embeddings appropriately. Other models don't have this hack
      self.offset = 2
      super().__init__(num_embeddings + self.offset, embedding_dim)
  
imports: import random
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from transformers.models.opt.configuration_opt import OPTConfig
label_desc: Use image node_id 1 to create a new OPTLearnedPositionalEmbedding object from inherited base classes: nn with example: obj = OPTLearnedPositionalEmbedding(num_embeddings, embedding_dim)