How to use the trains.Task.init function in trains

# TRAINS - Example of tensorboard with tensorflow (without any actual training)
#
import tensorflow as tf
import numpy as np
from PIL import Image

from trains import Task
task = Task.init(project_name='examples', task_name='tensorboard toy example')


k = tf.placeholder(tf.float32)

# Make a normal distribution, with a shifting mean
mean_moving_normal = tf.random_normal(shape=[1000], mean=(5*k), stddev=1)
# Record that distribution into a histogram summary
tf.summary.histogram("normal/moving_mean", mean_moving_normal)
tf.summary.scalar("normal/value", mean_moving_normal[-1])

# Make a normal distribution with shrinking variance
variance_shrinking_normal = tf.random_normal(shape=[1000], mean=0, stddev=1-(k))
# Record that distribution too
tf.summary.histogram("normal/shrinking_variance", variance_shrinking_normal)
tf.summary.scalar("normal/variance_shrinking_normal", variance_shrinking_normal[-1])

import sys
from argparse import ArgumentParser

from absl import app
from absl import flags
from absl import logging

from trains import Task


FLAGS = flags.FLAGS

flags.DEFINE_string('echo', None, 'Text to echo.')
flags.DEFINE_string('another_str', 'My string', 'A string', module_name='test')

task = Task.init(project_name='examples', task_name='hyper-parameters example')

flags.DEFINE_integer('echo3', 3, 'Text to echo.')
flags.DEFINE_string('echo5', '5', 'Text to echo.', module_name='test')


parameters = {
    'list': [1, 2, 3],
    'dict': {'a': 1, 'b': 2},
    'tuple': (1, 2, 3),
    'int': 3,
    'float': 2.2,
    'string': 'my string',
}
parameters = task.connect(parameters)

# adding new parameter after connect (will be logged as well)