tensor2tensor/models/image_transformer_2d.py [75:98]:
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    hparams = copy.copy(self._hparams)
    targets = features["targets"]
    inputs = features["inputs"]
    if not (tf.get_variable_scope().reuse or
            hparams.mode == tf.estimator.ModeKeys.PREDICT):
      tf.summary.image("inputs", inputs, max_outputs=1)
      tf.summary.image("targets", targets, max_outputs=1)

    encoder_input = cia.prepare_encoder(inputs, hparams)
    encoder_output = cia.transformer_encoder_layers(
        encoder_input,
        hparams.num_encoder_layers,
        hparams,
        attention_type=hparams.enc_attention_type,
        name="encoder")
    decoder_input, rows, cols = cia.prepare_decoder(
        targets, hparams)
    decoder_output = cia.transformer_decoder_layers(
        decoder_input,
        encoder_output,
        hparams.num_decoder_layers,
        hparams,
        attention_type=hparams.dec_attention_type,
        name="decoder")
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tensor2tensor/models/image_transformer_2d.py [111:134]:
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    hparams = copy.copy(self._hparams)
    targets = features["targets"]
    inputs = features["inputs"]
    if not (tf.get_variable_scope().reuse or
            hparams.mode == tf.estimator.ModeKeys.PREDICT):
      tf.summary.image("inputs", inputs, max_outputs=1)
      tf.summary.image("targets", targets, max_outputs=1)

    encoder_input = cia.prepare_encoder(inputs, hparams)
    encoder_output = cia.transformer_encoder_layers(
        encoder_input,
        hparams.num_encoder_layers,
        hparams,
        attention_type=hparams.enc_attention_type,
        name="encoder")
    decoder_input, rows, cols = cia.prepare_decoder(
        targets, hparams)
    decoder_output = cia.transformer_decoder_layers(
        decoder_input,
        encoder_output,
        hparams.num_decoder_layers,
        hparams,
        attention_type=hparams.dec_attention_type,
        name="decoder")
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