code: def gather_continuous_embeddings(
      self,
      input_ids: torch.Tensor,
      word_embeddings: torch.Tensor,
      image_embeddings: torch.Tensor,
  ) -> torch.Tensor:
      start_indices = (
          input_ids == self.config.image_token_id
      ).nonzero()[:, 1]
      embeddings = []
  
      for sample_idx, start_idx in enumerate(start_indices.tolist()):
          embeddings.append(
              torch.cat(
                  (
                      word_embeddings[sample_idx, :start_idx],
                      image_embeddings[sample_idx],
                      word_embeddings[sample_idx, start_idx + 1 :],
                  ),
                  dim=0,
              )
          )
  
      return torch.stack(embeddings, dim=0)
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from torchvision.transforms import *
  from transformers import AutoConfig, AutoTokenizer
  from transformers.configuration_utils import PretrainedConfig
  from transformers.modeling_outputs import CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.models.auto.modeling_auto import AutoModel, AutoModelForCausalLM
  from transformers.processing_utils import ProcessorMixin
  from transformers.tokenization_utils_base import BatchEncoding
  from uform.models import VisualEncoder
label_desc: Use image node_id 5 for calling the VLMForCausalLM obj's underlying member method code with example usage: obj.gather_continuous_embeddings(input_ids, word_embeddings, image_embeddings) and returns: torch