class_docstr: An embedding module represents a sequence of sentences
  
  Args:
      lstm_config (BiLSTM.Config): config of the lstm layer
      num_embeddings (int): Total number of words/tokens (vocabulary size).
      embedding_dim (int): Size of embedding vector.
      embeddings_weight (torch.Tensor): Pretrained weights to initialize the
          embedding table with.
      init_range (List[int]): Range of uniform distribution to initialize the
          weights with if `embeddings_weight` is None.
      unk_token_idx (int): Index of UNK token in the word vocabulary.
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code: def freeze(self):
      for param in self.word_embedding.parameters():
          param.requires_grad = False
  
file_path: pytext/pytext/models/embeddings/word_seq_embedding.py
imports: from typing import List, Optional
  import torch
  from pytext.data.tensorizers import Tensorizer
  from pytext.models.embeddings.embedding_base import EmbeddingBase
  from pytext.models.representations.bilstm import BiLSTM
  from pytorch.text.fb.utils.config_utils import EmbedInitStrategy
  from pytorch.text.fb.utils.embeddings import PretrainedEmbedding
  from torch import nn
  from torch.utils.tensorboard import SummaryWriter
label_desc: Use image node_id 5 for calling the WordSeqEmbedding obj's underlying member method code with example usage: obj.freeze() without return types