class_docstr: Base class for (initial) graph embedding construction.
  
  ...
  
  Attributes
  ----------
  feat : dict
      Raw features of graph nodes and/or edges.
  
  Methods
  -------
  forward(raw_text_data)
      Generate dynamic graph topology and embeddings.
class_docstr_tok: ["Base","class","for","(","initial",")","graph","embedding","construction",".","...","Attributes","--","--","--","--","--","feat",":","dict","Raw","features","of","graph","nodes","and\/or","edges",".","Methods","--","--","--","-","forward","(","raw_text_data",")","Generate","dynamic","graph","topology","and","embeddings","."]
code: def __init__(self):
      super(EmbeddingConstructionBase, self).__init__()
  
imports: import torch
  from torch import nn
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from ...data.data import from_batch
  from ..utils.bert_utils import convert_text_to_bert_features, extract_bert_hidden_states
  from ..utils.generic_utils import dropout_fn
  from ..utils.padding_utils import pad_4d_vals
  from ..utils.vocab_utils import Vocab
label_desc: Use image node_id 1 to create a new EmbeddingConstructionBase object from inherited base classes: nn with example: obj = EmbeddingConstructionBase()