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def main():
    # Insert standard model card construction here 
    
    model_card.model_parameters.data.append(mctlib.Dataset())
    model_card.model_parameters.data[1].graphics.description = (
      f'{len(X_test)} rows with {len(X_test.columns)} features')
    model_card.model_parameters.data[1].graphics.collection = [
        mctlib.Graphic(image=mean_radius_test),
        mctlib.Graphic(image=mean_texture_test)
    ]

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    <div class="row">
    {% if thingstoknow and (thingstoknow.intervention) %}
    <div class="col card">
      <h2>Things to Know</h2>
        {% if thingstoknow.intervention %}
          <h3>Intervention</h3>
            {{ render_considerations(thingstoknow.intervention) }}
        {% endif %}

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# CUSTOM CLASSES
@dataclasses.dataclass
class Intervention(BaseModelCardField):
    """Assistance or Assessment
    Attributes:
    description: How the application affects humans.
    """
    description: Optional[str] = None

    _proto_type: dataclasses.InitVar[type(

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    # Generate Model Card.
    jinja_env = jinja2.Environment(
        loader=self._jinja_loader(template_dir),
        autoescape=True,
        auto_reload=True,
        cache_size=0)
    template = jinja_env.get_template(template_file)
    model_card_file_content = template.render(
        model_details=model_card.model_details,
        model_parameters=model_card.model_parameters,

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// CUSTOM FIELDS
// How the model affects humans.
message Intervention {
  // Assistance or Assessment
  optional string description = 1;
}
message Thingstoknow {
  // Things you should know
  repeated Intervention intervention = 1;

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def main():
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument('topic', type=str,
                        help='Name of the Kafka topic to stream.')

    args = parser.parse_args()

    conf = {'bootstrap.servers': 'localhost:9092',
            'default.topic.config': {'auto.offset.reset': 'smallest'},
            'group.id': socket.gethostname()}

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# Process a Kafka message

def msg_process(msg):
    # Print the current time and the message.
    time_start = time.strftime("%Y-%m-%d %H:%M:%S")
    val = msg.value()
    dval = json.loads(val)
    print(time_start, dval)

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def main():
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument('filename', type=str,
                        help='Time series csv file.')
    parser.add_argument('topic', type=str,
                        help='Name of the Kafka topic to stream.')
    parser.add_argument('--speed', type=float, default=1, required=False,
                        help='Speed up time series by a given multiplicative factor.')
    args = parser.parse_args()

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def wait_model_ready(model_name, model_version):
    client = MlflowClient()
    for _ in range(10):
        model_version_details = client.get_model_version(name=model_name,
                                                         version=model_version)
        status = ModelVersionStatus.from_string(model_version_details.status)
        print("Model status: %s" % ModelVersionStatus.to_string(status))
        if status == ModelVersionStatus.READY:
            return True

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model_path = "clf-model"

# Load a standard machine learning dataset
cancer = load_breast_cancer()

df = pd.DataFrame(cancer['data'], columns=cancer['feature_names'])
df['target'] = cancer['target']

# Optionally write out a subset of the data, used in this tutorial for inference with the API
if args.outputTestData: