class_docstr: This module learns positional embeddings up to a fixed maximum size.
code: def __init__(self, weights):
      super().__init__()
      self.offset = 2
      self.weight = nn.Parameter(
          weights.get_tensor("model.decoder.embed_positions.weight")
      )
  
imports: import random
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers import OPTConfig
  from text_generation_server.utils.layers import FastLinear, TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, TensorParallelHead
label_desc: Use image node_id 1 to create a new OPTLearnedPositionalEmbedding object from inherited base classes: nn with example: obj = OPTLearnedPositionalEmbedding(weights)