class_docstr: Representation for a sequence of sentences
  Each sentence will be embedded with a DocNN model,
  then all the sentences are embedded with another DocNN/BiLSTM model
code: def forward(
      self,
      embedded_seqs: torch.Tensor,
      seq_lengths: torch.Tensor,
      *args,
  ) -> torch.Tensor:
      # embedded_seqs: (bsz, max_num_sen, max_seq_len, dim)
      (
          bsz,
          max_num_sen,
          max_seq_len,
          dim,
      ) = torch.onnx.operators.shape_as_tensor(embedded_seqs)
      rep = self.doc_rep(
          torch.onnx.operators.reshape_from_tensor_shape(
              embedded_seqs,
              torch.cat(
                  (
                      (bsz * max_num_sen).view(1),
                      max_seq_len.view(1),
                      dim.view(1),
                  )
              ),
          )
      )
      sentence_reps = torch.onnx.operators.reshape_from_tensor_shape(
          rep,
          torch.cat(
              (
                  bsz.view(1),
                  max_num_sen.view(1),
                  torch.tensor(self.doc_representation_dim).view(1),
              )
          ),
      )
      if isinstance(self.seq_rep, BiLSTMDocAttention):
          return self.seq_rep(
              embedded_tokens=sentence_reps, seq_lengths=seq_lengths
          )
      else:
          return self.seq_rep(embedded_tokens=sentence_reps)
  
file_path: pytext/pytext/models/representations/seq_rep.py
imports: from typing import Union
  import torch
  from pytext.config import ConfigBase
  from pytext.models.module import create_module
  from pytext.utils.usage import log_class_usage
  from .bilstm_doc_attention import BiLSTMDocAttention
  from .docnn import DocNNRepresentation
  from .representation_base import RepresentationBase
label_desc: Use image node_id 2 for calling the SeqRepresentation obj's underlying member method code with example usage: obj.forward(embedded_seqs, seq_lengths) and returns: self, self