class_docstr: Stochastic Variational Video Prediction Without Learned Prior.
class_docstr_tok: ["Stochastic","Variational","Video","Prediction","Without","Learned","Prior","."]
docstr: Stacked LSTM layers with FC layers as input and output embeddings.
  
  Args:
    inputs: input tensor
    states: a list of internal lstm states for each layer
    hidden_size: number of lstm units
    output_size: size of the output
    nlayers: number of lstm layers
  Returns:
    net: output of the network
    skips: a list of updated lstm states for each layer
docstr_tok: ["Stacked","LSTM","layers","with","FC","layers","as","input","and","output","embeddings",".","Args",":","inputs",":","input","tensor","states",":","a","list","of","internal","lstm","states","for","each","layer","hidden_size",":","number","of","lstm","units","output_size",":","size","of","the","output","nlayers",":","number","of","lstm","layers","Returns",":","net",":","output","of","the","network","skips",":","a","list","of","updated","lstm","states","for","each","layer"]
code: def stacked_lstm(
      self, inputs, states, hidden_size, output_size, nlayers
  ):
      """Stacked LSTM layers with FC layers as input and output embeddings.
  
      Args:
        inputs: input tensor
        states: a list of internal lstm states for each layer
        hidden_size: number of lstm units
        output_size: size of the output
        nlayers: number of lstm layers
      Returns:
        net: output of the network
        skips: a list of updated lstm states for each layer
      """
      net = inputs
      net = tfl.dense(net, hidden_size, activation=None, name="af1")
      for i in range(nlayers):
          net, states[i] = common_video.basic_lstm(
              net, states[i], hidden_size, name="alstm%d" % i
          )
      net = tfl.dense(
          net, output_size, activation=tf.nn.tanh, name="af2"
      )
      return net, states
  
file_path: tensor2tensor/tensor2tensor/models/video/emily.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.models.video import sv2p
  from tensor2tensor.models.video import sv2p_params
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 3 for calling the NextFrameEmily obj's underlying member method code with example usage: obj.stacked_lstm(inputs, states, hidden_size, output_size, nlayers) and returns: net,  states