class_docstr: This class is a Torchscript-friendly version of
  pytext.models.embeddings.EmbeddingList. The main differences are that it
  requires input arguments to be passed in as a list of Tensors, since
  Torchscript does not allow variable arguments, and that it only supports
  concat mode, since Torchscript does not support return value variance.
code: def _adapt_embedding(embedding: torch.nn.Module) -> torch.nn.Module:
      param_count = len(
          [
              p
              for p in signature(embedding.forward).parameters.values()
              if p.default == inspect._empty
          ]
      )
  
      if param_count == 1:
          return ScriptableEmbeddingList.Wrapper1(embedding)
      elif param_count == 2:
          return ScriptableEmbeddingList.Wrapper2(embedding)
      elif param_count == 3:
          return ScriptableEmbeddingList.Wrapper3(embedding)
      raise AssertionError(
          f"Unsupported parameter count {param_count}. If a new embedding "
          "class has been added, you will need to add support in this class."
      )
  
file_path: pytext/pytext/models/embeddings/scriptable_embedding_list.py
imports: import inspect
  from inspect import signature
  from typing import Iterable, List, Tuple
  import torch
  from pytext.models.embeddings import EmbeddingBase
  from torch.utils.tensorboard import SummaryWriter
label_desc: Use image node_id 1 for calling the ScriptableEmbeddingList obj's underlying member method code with example usage: obj._adapt_embedding(embedding) and returns: ScriptableEmbeddingList, ScriptableEmbeddingList, ScriptableEmbeddingList