import tensorflow as tf

    output_bias = tf.get_variable(
        "output_bias", [num_labels], initializer=tf.zeros_initializer()
    )
    with tf.variable_scope("loss"):
        if is_training:
            output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
        output_layer = tf.reshape(output_layer, [-1, hidden_size])
        logits = tf.matmul(output_layer, output_weight, transpose_b=True)
        logits = tf.nn.bias_add(logits, output_bias)
        logits = tf.reshape(logits, [-1, FLAGS.max_seq_length, 11])
        log_probs = tf.nn.log_softmax(logits, axis=-1)

        # labels = tf.cast(labels,dtype=tf.float32)
        one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
        per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
        loss = tf.reduce_sum(per_example_loss)
        return (loss, per_example_loss, logits)


def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
                     num_train_steps, num_warmup_steps, use_tpu,
                     use_one_hot_embeddings):
    def model_fn(features, labels, mode, params):
        tf.logging.info("*** Features ***")
        for name in sorted(features.keys()):
            tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))
        input_ids = features["input_ids"]
        input_mask = features["input_mask"]
        segment_ids = features["segment_ids"]
        label_ids = features["label_ids"]
