transformer 기계번역 강의 오류 질문

class Encoder(tf.keras.layers.Layer):
  def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
               maximum_position_encoding, rate=0.1):
    super(Encoder, self).__init__()

    self.d_model = d_model
    self.num_layers = num_layers

    self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
    self.pos_encoding = positional_encoding(maximum_position_encoding,
                                            self.d_model)

    self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate)
                       for _ in range(num_layers)]

    self.dropout = tf.keras.layers.Dropout(rate)

  def call(self, x,  training, mask):

    seq_len = tf.shape(x)[1]

    # adding embedding and position encoding.
    x = self.embedding(x)  # (batch_size, input_seq_len, d_model)
    x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
    x += self.pos_encoding[:, :seq_len, :]

    x = self.dropout(x, training=training)

    for i in range(self.num_layers):
      x = self.enc_layers[i](x, training, mask)

    return x  # (batch_size, input_seq_len, d_model)

위와 같이 클래스 인코더가 정의 되어있고
아래와 같이 테스트를 진행했습니다
오류가 발생하여 [training=False, mask=None] 으로 수정하여 진행을 해도 같은 오류가 발생합니다.

sample_encoder = Encoder(num_layers=2, d_model=512, num_heads=8,
                         dff=2048, input_vocab_size=8500,
                         maximum_position_encoding=10000)
temp_input = tf.random.uniform((64, 62), dtype=tf.int64, minval=0, maxval=200)

sample_encoder_output = sample_encoder(temp_input, training=False, mask=None)

print(sample_encoder_output.shape)  # (batch_size, input_seq_len, d_model)

오류내용

ValueError: Exception encountered when calling Encoder.call().

Only input tensors may be passed as positional arguments. The following argument value should be passed as a keyword argument: False (of type <class 'bool'>)

Arguments received by Encoder.call():
  • x=tf.Tensor(shape=(64, 62), dtype=int64)
  • training=False
  • mask=None

문제가 무엇일까요 ㅜㅠ

sample_encoder_output = sample_encoder(temp_input, training=False, mask=None)