code: def perf_transformer_encode(
      encoder_function,
      inputs,
      target_space,
      hparams,
      baseline,
      attention_weights=None,
      features=None,
      losses=None,
      prepare_encoder_fn=None,
      **kwargs,
  ):
      """Encoding for performance autoencoder, which mean-aggregates across time.
  
      Args:
        encoder_function: the encoder function
        inputs: Transformer inputs [batch_size, input_length, 1, hidden_dim] which
          will be flattened along the two spatial dimensions.
        target_space: scalar, target space ID.
        hparams: hyperparameters for model.
        baseline: if True, does not mean-aggregate the encoder output.
        attention_weights: weight to store attention to.
        features: optionally pass the entire features dictionary as well. This is
          needed now for "packed" datasets.
        losses: optional list onto which to append extra training losses
        prepare_encoder_fn: optional, alternative to transformer_prepare_encoder.
        **kwargs: additional arguments to pass to encoder_function
  
      Returns:
        Tuple of:
            encoder_output: Encoder representation.
                [batch_size, input_length, hidden_dim]
            encoder_decoder_attention_bias: Bias and mask weights for
                encoder-decoder attention. [batch_size, input_length]
      """
      inputs = common_layers.flatten4d3d(inputs)
  
      if not prepare_encoder_fn:
          prepare_encoder_fn = transformer_prepare_encoder
      (
          encoder_input,
          self_attention_bias,
          encoder_decoder_attention_bias,
      ) = prepare_encoder_fn(
          inputs,
          target_space,
          hparams,
          features=features,
          reuse_target_embedding=tf.AUTO_REUSE,
      )
  
      mlperf_log.transformer_print(
          key=mlperf_log.MODEL_HP_LAYER_POSTPROCESS_DROPOUT,
          value=hparams.layer_prepostprocess_dropout,
          hparams=hparams,
      )
  
      encoder_input = tf.nn.dropout(
          encoder_input, 1.0 - hparams.layer_prepostprocess_dropout
      )
  
      attn_bias_for_padding = None
      # Otherwise the encoder will just use encoder_self_attention_bias.
      if hparams.unidirectional_encoder:
          attn_bias_for_padding = encoder_decoder_attention_bias
  
      encoder_output = encoder_function(
          encoder_input,
          self_attention_bias,
          hparams,
          name="encoder",
          nonpadding=features_to_nonpadding(features, "inputs"),
          save_weights_to=attention_weights,
          make_image_summary=not common_layers.is_xla_compiled(),
          losses=losses,
          attn_bias_for_padding=attn_bias_for_padding,
          **kwargs,
      )
  
      if not baseline:
          encoder_output = tf.math.reduce_mean(
              encoder_output, axis=1, keep_dims=True
          )
          encoder_decoder_attention_bias = tf.math.reduce_mean(
              encoder_decoder_attention_bias, axis=-1, keep_dims=True
          )
  
      return encoder_output, encoder_decoder_attention_bias
  
imports: from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import modalities
  from tensor2tensor.layers import transformer_layers
  from tensor2tensor.models.transformer import Transformer, transformer_decoder, transformer_prepare_encoder, transformer_prepare_decoder, features_to_nonpadding, _init_transformer_cache
  from tensor2tensor.utils import mlperf_log
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 1 for calling a global function with example usage: perf_transformer_encode(encoder_function, inputs, target_space, hparams, baseline, attention_weights, features, losses, prepare_encoder_fn) and returns: encoder_output,  encoder_decoder_attention_bias