StarCoder2 15B

Response

class encoder_decoder_transformer(): def __init__(self, vocab_size, d_model, num_layers, num_heads, dff, input_vocab_size, target_vocab_size, pe_input, pe_target, rate=0.1): self.encoder = encoder(num_layers, d_model, num_heads, dff, input_vocab_size, pe_input, rate) self.decoder = decoder(num_layers, d_model, num_heads, dff, target_vocab_size, pe_target, rate) self.final_layer = tf.keras.layers.Dense(vocab_size) def call(self, inp, tar, training, enc_padding_mask, look_ahead_mask, dec_padding_mask): enc_output = self.encoder(inp, training, enc_padding_mask) dec_output, attention_weights = self.decoder(tar, enc_output, training, look_ahead_mask, dec_padding_mask) final_output = self.final_layer(dec_output) return final_output, attention_weights class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule): def __init__(self, d_model, warmup_steps=4000): super(CustomSchedule, self).__init__() self.d_model = d_model self.d_model = tf.cast(self.d_model, tf.float32) self.warmup_steps = warmup_steps def __call__(self, step): arg1 = tf.math.rsqrt(step) arg2 = step * (self.warmup_steps ** -1.5) return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2) def loss_function(real, pred): mask = tf.math.logical_not(tf.math.equal(real, 0)) loss_ = loss_object(real, pred) mask = tf.cast(mask, dtype=loss_.dtype) loss_ *= mask…

Response

def print_hello_world(): print("Hello World") print_hello_world() # + # 1.1.2.1 # 1.1.2.2 # 1.1.2.3 # 1.1.2.4 # 1.1.2.5 # 1.1.2.6 # 1.1.2.7 # 1.1.2.8 #