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 layer as input and gaussian as output.
  
  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
    name: the lstm name for scope definition
  Returns:
    mu: mean of the predicted gaussian
    logvar: log(var) of the predicted gaussian
    skips: a list of updated lstm states for each layer
docstr_tok: ["Stacked","LSTM","layers","with","FC","layer","as","input","and","gaussian","as","output",".","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","name",":","the","lstm","name","for","scope","definition","Returns",":","mu",":","mean","of","the","predicted","gaussian","logvar",":","log","(","var",")","of","the","predicted","gaussian","skips",":","a","list","of","updated","lstm","states","for","each","layer"]
code: def lstm_gaussian(
      self, inputs, states, hidden_size, output_size, nlayers, name
  ):
      """Stacked LSTM layers with FC layer as input and gaussian as output.
  
      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
        name: the lstm name for scope definition
      Returns:
        mu: mean of the predicted gaussian
        logvar: log(var) of the predicted gaussian
        skips: a list of updated lstm states for each layer
      """
      net = inputs
      net = tfl.dense(
          net, hidden_size, activation=None, name="%sf1" % name
      )
      for i in range(nlayers):
          net, states[i] = common_video.basic_lstm(
              net, states[i], hidden_size, name="%slstm%d" % (name, i)
          )
      mu = tfl.dense(
          net, output_size, activation=None, name="%sf2mu" % name
      )
      logvar = tfl.dense(
          net, output_size, activation=None, name="%sf2log" % name
      )
      return mu, logvar, 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 4 for calling the NextFrameEmily obj's underlying member method code with example usage: obj.lstm_gaussian(inputs, states, hidden_size, output_size, nlayers, name) and returns: mu,  logvar,  states