code: def forward(self, input_ids):
      batch_fixes = self.embeddings.fixes
      self.embeddings.fixes = None
  
      inputs_embeds = self.wrapped(input_ids)
  
      if (
          batch_fixes is None
          or len(batch_fixes) == 0
          or max([len(x) for x in batch_fixes]) == 0
      ):
          return inputs_embeds
  
      vecs = []
      for fixes, tensor in zip(batch_fixes, inputs_embeds):
          for offset, embedding in fixes:
              vec = (
                  embedding.vec[self.textual_inversion_key]
                  if isinstance(embedding.vec, dict)
                  else embedding.vec
              )
              emb = devices.cond_cast_unet(vec)
              emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
              tensor = torch.cat(
                  [
                      tensor[0 : offset + 1],
                      emb[0:emb_len],
                      tensor[offset + 1 + emb_len :],
                  ]
              )
  
          vecs.append(tensor)
  
      return torch.stack(vecs)
  
imports: import torch
  from torch.nn.functional import silu
  from types import MethodType
  from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet, patches
  from modules.hypernetworks import hypernetwork
  from modules.shared import cmd_opts
  from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, xlmr_m18
  import ldm.modules.attention
  import ldm.modules.diffusionmodules.model
  import ldm.modules.diffusionmodules.openaimodel
  import ldm.models.diffusion.ddpm
  import ldm.models.diffusion.ddim
  import ldm.models.diffusion.plms
  import ldm.modules.encoders.modules
  import sgm.modules.attention
  import sgm.modules.diffusionmodules.model
  import sgm.modules.diffusionmodules.openaimodel
  import sgm.modules.encoders.modules
label_desc: Use image node_id 2 for calling the EmbeddingsWithFixes obj's underlying member method code with example usage: obj.forward(input_ids) and returns: torch, inputs_embeds