class Component extends React.Component {
  onClick = async () => { };
}
Honestly, any girl that uses those kind of phrases are usually the worst people to have any relationship with. My ex was a lot to deal with, all of her ''closest'' friend keep her at a safe distance and many of them just drift away from her or ghost her because of how toxic she can get. 

 Honestly, she would always tell me how those friends ''backstab'' her or how they were evil, etc. I never once put the pieces of the puzzle together to think ''Why people keep leaving her?''Yours is fine as well, it just sounds a bit archaic, as in the past few hundred years we haven't been using the genitive in cases like this, it's simply "a medve egyik bocsa", not "medvének", but if you read old texts, you can see structures like "a városnak egyik tornya" etc. and that is 100% correct, in fact, that's the correct version in theory. Also, even though "fára mászik fel" can be considered correct, a native would simply say "fára mászik", "fel" is redundant here, it is only used if the action is finished, so "a bocs fára mászik", but "felmászott" if it climbed all the way up. Or you can also use it if you want to emphasize that someone is climbing, or will climb all the way up. "A bocs fára mászik" means the cub's climbing a tree", "a bocs felmászik a fára" means "the cub climbs on top of the tree".G&S was a british commercial distributor with operations in manyu countries around the world, Spain among them. With an impressive history of success since 1884, in 2001 a companywide crisis particularly affected Spain's operations, and placed its leadership team in a multifaceted challenge. This case study offers the opportunity to think about how the HR department of Spain, leaded by Nicole Newman, would be able to face this challenge from the perspective of customer service. This all happens within a restricted budgetary situation, and with high ROI expectations for any new initatives that tackle the problems. To make things worse, employee morale was at dangerously low levels...
<commit_before># Copyright (c) 2010 <NAME> <<EMAIL>>
# License: Simplified BSD
"""Glue code to load http://mlcomp.org data as a scikit.learn dataset"""

import os
from scikits.learn.datasets.base import Bunch
from scikits.learn.features.text import HashingVectorizer


def load_document_classification(dataset_path, metadata, set_, **kw):
    """Loader implementation for the DocumentClassification format"""
    target = []
    target_names = {}
    vectorizer = kw.get('vectorizer', HashingVectorizer())

    dataset_path = os.path.join(dataset_path, set_)
    folders = [f for f in sorted(os.listdir(dataset_path))
               if os.path.isdir(os.path.join(dataset_path, f))]
    for label, folder in enumerate(folders):
        target_names[label] = folder
        folder_path = os.path.join(dataset_path, folder)
        documents = [os.path.join(folder_path, d)
                     for d in sorted(os.listdir(folder_path))]
        vectorizer.vectorize(documents)
        target.extend(len(documents) * [label])

    return Bunch(data=vectorizer.get_vectors(), target=target,
                 target_names=target_names, DESCR=metadata.get('description'))


LOADERS = {
    'DocumentClassification': load_document_classification,
    # TODO: implement the remaining domain formats
}


def load_mlcomp(name_or_id, mlcomp_root=None, set_="raw", **kwargs):
    """Load a datasets as downloaded from http://mlcomp.org

    Parameters
    ----------

    name_or_id : the integer id or the string name metadata of the MLComp
                 dataset to load

    mlcomp_root : the filesystem path to the root folder where MLComp datasets
                  are stored, if mlcomp_root is None, the MLCOMP_DATASETS_HOME
                  environment variable is looked up instead.

    set_ : select the portion to load: 'train', 'test' or 'raw'

    **kwargs : domain specific kwargs to be passed to the dataset loader.

    Returns
    -------

    data : Bunch
        Dictionnary-like object, the interesting attributes are:
        'data', the data to learn, 'target', the classification labels,
        'target_names', the meaning of the labels, and 'DESCR', the
        full description of the dataset.

    Note on the lookup process: depending on the type of name_or_id,
    will choose between integer id lookup or metadata name lookup by
    looking at the unzipped archives and metadata file.

    TODO: implement zip dataset loading too
    """

    if mlcomp_root is None:
        try:
            mlcomp_root = os.environ['MLCOMP_DATASETS_HOME']
        except KeyError:
            raise ValueError("MLCOMP_DATASETS_HOME env variable is undefined")

    mlcomp_root = os.path.expanduser(mlcomp_root)
    mlcomp_root = os.path.abspath(mlcomp_root)
    mlcomp_root = os.path.normpath(mlcomp_root)

    if not os.path.exists(mlcomp_root):
        raise ValueError("Could not find folder: " + mlcomp_root)

    # dataset lookup
    if isinstance(name_or_id, int):
        # id lookup
        dataset_path = os.path.join(mlcomp_root, str(name_or_id))
    else:
        # assume name based lookup
        dataset_path = None
        expected_name_line = "name: " + name_or_id
        for dataset in os.listdir(mlcomp_root):
            metadata_file = os.path.join(mlcomp_root, dataset,'metadata')
            if not os.path.exists(metadata_file):
                continue
            for line in file(metadata_file):
                if line.strip() == expected_name_line:
                    dataset_path = os.path.join(mlcomp_root, dataset)
                    break
        if dataset_path is None:
            raise ValueError("Could not find dataset with metadata line: " +
                             expected_name_line)

    # loading the dataset metadata
    metadata = dict()
    metadata_file = os.path.join(dataset_path,'metadata')
    if not os.path.exists(metadata_file):
        raise ValueError(dataset_path +'is not a valid MLComp dataset')
    for line in file(metadata_file):
        if ":" in line:
            key, value = line.split(":", 1)
            metadata[key.strip()] = value.strip()

    format = metadata.get('format', 'unknow')
    loader = LOADERS.get(format)
    if loader is None:
        raise ValueError("No loader implemented for format: " + format)
    return loader(dataset_path, metadata, set_=set_, **kwargs)


if __name__ == "__main__":
    twentynews = load_mlcomp('20news-18828')
<commit_msg>make it easy to find the raw source document<commit_after># Copyright (c) 2010 <NAME> <<EMAIL>>
# License: Simplified BSD
"""Glue code to load http://mlcomp.org data as a scikit.learn dataset"""

import os
from scikits.learn.datasets.base import Bunch
from scikits.learn.features.text import HashingVectorizer


def load_document_classification(dataset_path, metadata, set_, **kw):
    """Loader implementation for the DocumentClassification format"""
    target = []
    target_names = {}
    filenames = []
    vectorizer = kw.get('vectorizer', HashingVectorizer())

    dataset_path = os.path.join(dataset_path, set_)
    folders = [f for f in sorted(os.listdir(dataset_path))
               if os.path.isdir(os.path.join(dataset_path, f))]
    for label, folder in enumerate(folders):
        target_names[label] = folder
        folder_path = os.path.join(dataset_path, folder)
        documents = [os.path.join(folder_path, d)
                     for d in sorted(os.