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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def from_path_and_array(cls, path, folder, y, classes=None, val_idxs=None, test_name=None, num_workers=8, tfms=(None,None), bs=64):
 """ Read in images given a sub-folder and their labels given a numpy array Arguments: path: a root path of the data (used for storing trained models, precomputed values, etc) folder: a name of the folder in which training images are contained. y: numpy array which contains target labels ordered by filenames. bs: batch size tfms: transformations (for data augmentations). e.g. output of `tfms_from_model` val_idxs: index of images to be used for validation. e.g. output of `get_cv_idxs`. If None, default arguments to get_cv_idxs are used. test_name: a name of the folder which contains test images. num_workers: number of workers Returns: ImageClassifierData """

        assert not (tfms[0] is None or tfms[1] is None), "please provide transformations for your train and validation sets"
        assert not (os.path.isabs(folder)), "folder needs to be a relative path"
        fnames = np.core.defchararray.add(f'{folder}/', sorted(os.listdir(f'{path}{folder}')))
        return cls.from_names_and_array(path, fnames, y, classes, val_idxs, test_name,
                num_workers=num_workers, tfms=tfms, bs=bs)