models_mnist/executor.py [219:243]:
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    loom_inputs['time'] = tf.placeholder(tf.int32, (None, 1), 'time')
    loom_inputs['round'] = tf.placeholder(tf.int32, (None, 1), 'round')

    return loom_inputs, used_inputs

  def _build_loom_types(self):
    """Method to build loom types for given setting.
    """

    params = self.params
    encode_size = params['lstm_size']

    # create and save loom types
    types = {}
    types['time'] = loom.TypeShape('int32', (1,), 'time')
    types['round'] = loom.TypeShape('int32', (1,), 'round')
    types['float'] = loom.TypeShape('float32', (1,))
    types['context'] = loom.TypeShape('float32', (encode_size,), 'context')
    types['align'] = loom.TypeShape('float32', (encode_size,), 'align')

    size = (params['num_rounds'], params['text_embed_size'])
    types['fact'] = loom.TypeShape('float32', size, 'fact')
    size = (params['num_rounds'], params['max_dec_len'],
            params['text_embed_size'])
    types['text'] = loom.TypeShape('float32', size, 'text')
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models_vd/executor.py [212:236]:
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    loom_inputs['time'] = tf.placeholder(tf.int32, (None, 1), 'time')
    loom_inputs['round'] = tf.placeholder(tf.int32, (None, 1), 'round')

    return loom_inputs, used_inputs

  def _build_loom_types(self):
    """Method to build loom types for given setting.
    """

    params = self.params
    encode_size = params['lstm_size']

    # create and save loom types
    types = {}
    types['time'] = loom.TypeShape('int32', (1,), 'time')
    types['round'] = loom.TypeShape('int32', (1,), 'round')
    types['float'] = loom.TypeShape('float32', (1,))
    types['context'] = loom.TypeShape('float32', (encode_size,), 'context')
    types['align'] = loom.TypeShape('float32', (encode_size,), 'align')

    size = (params['num_rounds'], params['text_embed_size'])
    types['fact'] = loom.TypeShape('float32', size, 'fact')
    size = (params['num_rounds'], params['max_dec_len'],
            params['text_embed_size'])
    types['text'] = loom.TypeShape('float32', size, 'text')
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