class_docstr: It is a MongoDB based manager that help you to manage data, network architecture, parameters and logging.
  
  Parameters
  -------------
  ip : str
      Localhost or IP address.
  port : int
      Port number.
  dbname : str
      Database name.
  username : str or None
      User name, set to None if you do not need authentication.
  password : str
      Password.
  project_name : str or None
      Experiment key for this entire project, similar with the repository name of Github.
  
  Attributes
  ------------
  ip, port, dbname and other input parameters : see above
      See above.
  project_name : str
      The given project name, if no given, set to the script name.
  db : mongodb client
      See ``pymongo.MongoClient``.
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docstr: Finds and returns all datasets from the database which matches the requirement.
  In some case, the data in a dataset can be stored separately for better management.
  
  Parameters
  ----------
  dataset_name : str
      The name/key of dataset.
  kwargs : other events
      Other events, such as description, author and etc (optional).
  
  Returns
  --------
  params : the parameters, return False if nothing found.
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code: def find_datasets(self, dataset_name=None, **kwargs):
      """Finds and returns all datasets from the database which matches the requirement.
      In some case, the data in a dataset can be stored separately for better management.
  
      Parameters
      ----------
      dataset_name : str
          The name/key of dataset.
      kwargs : other events
          Other events, such as description, author and etc (optional).
  
      Returns
      --------
      params : the parameters, return False if nothing found.
  
      """
  
      self._fill_project_info(kwargs)
      if dataset_name is None:
          raise Exception(
              "dataset_name is None, please give a dataset name"
          )
      kwargs.update({"dataset_name": dataset_name})
  
      s = time.time()
      pc = self.db.Dataset.find(kwargs)
  
      if pc is not None:
          dataset_id_list = pc.distinct("dataset_id")
          dataset_list = []
          for (
              dataset_id
          ) in dataset_id_list:  # you may have multiple Buckets files
              tmp = self.dataset_fs.get(dataset_id).read()
              dataset_list.append(self._deserialization(tmp))
      else:
          print(
              "[Database] FAIL! Cannot find any dataset: {}".format(
                  kwargs
              )
          )
          return False
  
      print(
          "[Database] Find {} datasets SUCCESS, took: {}s".format(
              len(dataset_list), round(time.time() - s, 2)
          )
      )
      return dataset_list
  
imports: import os
  import pickle
  import sys
  import time
  from datetime import datetime
  import numpy
  import tensorflow
  import gridfs
  import pymongo
  from tensorlayer import logging
  from tensorlayer.files import assign_weights, del_folder, exists_or_mkdir, load_hdf5_to_weights, save_weights_to_hdf5, static_graph2net
label_desc: Use image node_id 11 for calling the TensorHub obj's underlying member method code with example usage: obj.find_datasets(dataset_name) and returns: dataset_list, False