code: def __init__(
      self,
      max_sequence_length,
      vocab,
      embedding_size,
      representation="dense",
      embeddings_trainable=True,
      pretrained_embeddings=None,
      force_embedding_size=False,
      embeddings_on_cpu=False,
      dropout=0.0,
      embedding_initializer=None,
  ):
      super().__init__()
      self.max_sequence_length = max_sequence_length
      self.embedding_size = embedding_size
      self.token_embed = EmbedSequence(
          vocab=vocab,
          embedding_size=embedding_size,
          max_sequence_length=max_sequence_length,
          representation=representation,
          embeddings_trainable=embeddings_trainable,
          pretrained_embeddings=pretrained_embeddings,
          force_embedding_size=force_embedding_size,
          embeddings_on_cpu=embeddings_on_cpu,
          dropout=dropout,
          embedding_initializer=embedding_initializer,
      )
      self.position_embed = nn.Embedding(
          num_embeddings=max_sequence_length,
          embedding_dim=self.token_embed.embedding_size,
      )
      self.register_buffer(
          "positions", torch.arange(0, max_sequence_length)
      )
  
imports: import logging
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from torch import nn
  from ludwig.constants import TYPE
  from ludwig.modules.initializer_modules import get_initializer
  from ludwig.utils.data_utils import load_pretrained_embeddings
  from ludwig.utils.torch_utils import get_torch_device, LudwigModule
label_desc: Use image node_id 1 to create a new TokenAndPositionEmbedding object from inherited base classes: LudwigModule with example: obj = TokenAndPositionEmbedding(max_sequence_length, vocab, embedding_size, representation, embeddings_trainable, pretrained_embeddings, force_embedding_size, embeddings_on_cpu, dropout, embedding_initializer)