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.
docstr: Get embeddings from all sub-embeddings and either concatenate them
  into one Tensor or return them in a tuple.
  
  Args:
      emb_input (type): Sequence of token level embeddings to combine.
          The inputs should match the size of configured embeddings. Each
          of them is a List of Tensors.
  
  Returns:
      torch.Tensor: a Tensor is returned by concatenating all embeddings.
code: def forward(
      self, emb_input: List[List[torch.Tensor]]
  ) -> torch.Tensor:
      """
      Get embeddings from all sub-embeddings and either concatenate them
      into one Tensor or return them in a tuple.
  
      Args:
          emb_input (type): Sequence of token level embeddings to combine.
              The inputs should match the size of configured embeddings. Each
              of them is a List of Tensors.
  
      Returns:
          torch.Tensor: a Tensor is returned by concatenating all embeddings.
  
      """
      # tokens dim: (bsz, max_seq_len) -> (bsz, max_seq_len, dim) OR
      # (bsz, max_num_sen, max_seq_len) -> (bsz, max_num_sen, max_seq_len, dim)
      # for seqnn
      if self.num_emb_modules != len(emb_input):
          raise Exception(
              f"expecting {self.num_emb_modules} embeddings, "
              + f"but got {len(emb_input)} input"
          )
      tensors = []
      for emb, start in zip(
          self.embeddings_list, self.input_start_indices
      ):
          tensors.append(emb(emb_input[start]))
  
      return torch.cat(tensors, 2)
  
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 3 for calling the ScriptableEmbeddingList obj's underlying member method code with example usage: obj.forward(emb_input) and returns: torch