code: def __init__(self, embedding_dim, encoder_output_dim):
      super().__init__()
      self.lstm = nn.LSTM(
          encoder_output_dim,  # text embedding dimension from the text encoder
          embedding_dim,  # fixed length output summary the lstm creates from the input
          batch_first=True,
          bidirectional=False,
      )
  
imports: import torch
  from torch import nn
  from torch.distributions.multivariate_normal import MultivariateNormal
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new TextSummary object from inherited base classes: nn with example: obj = TextSummary(embedding_dim, encoder_output_dim)