class_docstr: Configurable seq2seq model used for Neural Architecture Search.
  
  Models are defined by 26 hparam fields. They are:
    - <encoder/decoder>_num_cells: The number of cells in the <encoder/decoder>.
    - <encoder/decoder>_<left/right>_layers: List of layers used the
                                             <encoder/decoder> <left/right>
                                             branch. For available layers, see
                                             the nas_layers.py file.
    - <encoder/decoder>_<left/right_inputs>: List of inputs to the
                                             <encoder/decoder> <left/right>
                                             layers. Each index i specifies the
                                             i_th layer's output with 0
                                             representing the cell input
                                             tensor.
    - <encoder/decoder>_<left/right>_output_dims: List of absolute output
                                                  dimensions for each layer.
    - <encoder/decoder>_<left/right>_activation: List of activations applied
                                                 after each layer.
                                                 ACTIVATION_MAP holds the valid
                                                 activations.
    - <encoder/decoder>_<left/right>_norms: List of norms applied before each
                                            layer. Must be either "layer_norm"
                                            or "none".
    - <encoder/decoder>_combiner_functions: List of functions used to combine
                                            each left/right branch pair.
                                            Options are listed in
                                            COMBINER_FUNCTIONS.
    - <encoder/decoder>_final_combiner_function: Function applied to combine
                                                 all the block outputs that are
                                                 not used as inputs to other
                                                 blocks. Options are listed in
                                                 COMBINER_FUNCTIONS.
  
  For an example of how to set these hparams, please see nas_seq2seq_base().
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docstr: Forced slow beam decode.
  
  Args:
    features: an map of string to `Tensor`.
    decode_length: an integer.  How many additional timesteps to decode.
    beam_size: number of beams.
    top_beams: an integer. How many of the beams to return.
    alpha: Float that controls the length penalty. larger the alpha, stronger
      the preference for longer translations.
    use_tpu: Whether or not TPU is being used.
  
  Returns:
    A dict of decoding results {
        "outputs": integer `Tensor` of decoded ids of shape
            [batch_size, <= decode_length] if beam_size == 1 or
            [batch_size, top_beams, <= decode_length].
        "scores": decoding log probs from the beam search,
            None if using greedy decoding (beam_size=1).
    }
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code: def _beam_decode(
      self,
      features,
      decode_length,
      beam_size,
      top_beams,
      alpha,
      use_tpu,
  ):
      """Forced slow beam decode.
  
      Args:
        features: an map of string to `Tensor`.
        decode_length: an integer.  How many additional timesteps to decode.
        beam_size: number of beams.
        top_beams: an integer. How many of the beams to return.
        alpha: Float that controls the length penalty. larger the alpha, stronger
          the preference for longer translations.
        use_tpu: Whether or not TPU is being used.
  
      Returns:
        A dict of decoding results {
            "outputs": integer `Tensor` of decoded ids of shape
                [batch_size, <= decode_length] if beam_size == 1 or
                [batch_size, top_beams, <= decode_length].
            "scores": decoding log probs from the beam search,
                None if using greedy decoding (beam_size=1).
        }
      """
      return self._beam_decode_slow(
          features, decode_length, beam_size, top_beams, alpha, use_tpu
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import six
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.models.neural_architecture_search import nas_layers
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import metrics
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 8 for calling the NasSeq2Seq obj's underlying member method code with example usage: obj._beam_decode(features, decode_length, beam_size, top_beams, alpha, use_tpu) and returns: self