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def create_example(image, path, example):
    feature = {
        "image": image_feature(image),
        "path": bytes_feature(path),
        "area": float_feature(example["area"]),
        "bbox": float_feature_list(example["bbox"]),
        "category_id": int64_feature(example["category_id"]),
        "id": int64_feature(example["id"]),
        "image_id": int64_feature(example["image_id"]),
    }

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def image_feature(value):
    """Returns a bytes_list from a string / byte."""
    return tf.train.Feature(
        bytes_list=tf.train.BytesList(value=[tf.io.encode_jpeg(value).numpy()])
    )


def bytes_feature(value):
    """Returns a bytes_list from a string / byte."""
    return tf.train.Feature(

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num_samples = 4096
num_tfrecods = len(annotations) // num_samples

if len(annotations) % num_samples:
    num_tfrecods += 1  # add one record if there are any remaining samples

if not os.path.exists(tfrecords_dir):
    os.makedirs(tfrecords_dir)  # creating TFRecords output folder

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with tracer.start_span("get-price") as span:
    coins = {}
    res = requests.get("https://api.coincap.io/v2/assets")
    span.set_tag("count", len(res.json()['data']))
    for coin in res.json()['data']:
        with tracer.start_span(coin['id'], child_of=span) as coin_span:
            print(f"Getting info for {coin['id']}")
            try:
                res = requests.get(f"https://api.coincap.io/v2/assets?search={coin['id']}")
                print(f"The price of {coin['id']} is {coin['priceUsd']}")

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import logging
import requests

from jaeger_client import Config


def init_tracer(service):
    logging.getLogger('').handlers = []
    logging.basicConfig(format='%(message)s', level=logging.DEBUG)

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for epoch in range(15):
    train_loss.reset_states()
    train_accuracy.reset_states()
    test_loss.reset_states()
    test_accuracy.reset_states()

    for images, labels in train_ds:
        train_step(images, labels)

    for test_images, test_labels in test_ds:

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loss_fn = keras.losses.CategoricalCrossentropy()
optimizer = keras.optimizers.Adam()

train_loss = keras.metrics.Mean(name='train_loss')
train_accuracy = keras.metrics.CategoricalAccuracy(name='train_accuracy')

test_loss = keras.metrics.Mean(name='test_loss')
test_accuracy = keras.metrics.CategoricalAccuracy(name='test_accuracy')

def train_step(images, labels):

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class MNIST(keras.Model):
    def __init__(self):
        super().__init__()
        self.conv_1 = layers.Conv2D(32, kernel_size=(3, 3), activation="relu")
        self.conv_2 = layers.Conv2D(64, kernel_size=(3, 3), activation="relu")
        self.max_pool = layers.MaxPooling2D(pool_size=(2, 2))
        self.flatten = layers.Flatten()
        self.dropout = layers.Dropout(0.5)
        self.out =  layers.Dense(num_classes, activation="softmax")

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train_ds = tf.data.Dataset.from_tensor_slices(
    (x_train, y_train)).shuffle(10000).batch(32)

test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

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input = layers.Input(input_shape)

x_1 = layers.Conv2D(32, kernel_size=(3, 3), activation="relu")(input)
x_1 = layers.MaxPooling2D(pool_size=(2, 2))(x_1)
x_1 = layers.Conv2D(64, kernel_size=(3, 3), activation="relu")(x_1)
x_1 = layers.MaxPooling2D(pool_size=(2, 2))(x_1)
x_1 = layers.Flatten()(x_1)

x_2 = layers.Conv2D(16, kernel_size=(3, 3), activation="relu")(input)
x_2 = layers.MaxPooling2D(pool_size=(2, 2))(x_2)