How to use the torchmeta.utils.data.MetaDataset function in torchmeta

import numpy as np

from torchmeta.utils.data import Task, MetaDataset
from torchmeta.toy.sinusoid import SinusoidTask


class SinusoidAndLine(MetaDataset):
    """
    Simple multimodal regression task, based on sinusoids and lines, as
    introduced in [1].

    Parameters
    ----------
    num_samples_per_task : int
        Number of examples per task.

    num_tasks : int (default: 1,000,000)
        Overall number of tasks to sample.

    noise_std : float, optional
        Amount of noise to include in the targets for each task. If `None`, then
        nos noise is included, and the target is either a sine function, or a
        linear function of the input.

def apply_wrapper(wrapper, task_or_dataset=None):
    if task_or_dataset is None:
        return wrapper

    from torchmeta.utils.data import MetaDataset
    if isinstance(task_or_dataset, Task):
        return wrapper(task_or_dataset)
    elif isinstance(task_or_dataset, MetaDataset):
        if task_or_dataset.dataset_transform is None:
            dataset_transform = wrapper
        else:
            dataset_transform = Compose([
                task_or_dataset.dataset_transform, wrapper])
        task_or_dataset.dataset_transform = dataset_transform
        return task_or_dataset
    else:
        raise NotImplementedError()

import numpy as np

from torchmeta.utils.data import Task, MetaDataset


class Harmonic(MetaDataset):
    """
    Simple regression task, based on the sum of two sine waves, as introduced
    in [1].

    Parameters
    ----------
    num_samples_per_task : int
        Number of examples per task.

    num_tasks : int (default: 5,000)
        Overall number of tasks to sample.

    noise_std : float, optional
        Amount of noise to include in the targets for each task. If `None`, then
        nos noise is included, and the target is the sum of 2 sine functions of
        the input.

import numpy as np

from torchmeta.utils.data import Task, MetaDataset


class Sinusoid(MetaDataset):
    """
    Simple regression task, based on sinusoids, as introduced in [1].

    Parameters
    ----------
    num_samples_per_task : int
        Number of examples per task.

    num_tasks : int (default: 1,000,000)
        Overall number of tasks to sample.

    noise_std : float, optional
        Amount of noise to include in the targets for each task. If `None`, then
        nos noise is included, and the target is a sine function of the input.

    transform : callable, optional

import os
import json
import h5py
import numpy as np
import torch
import copy

from torchmeta.utils.data import Task, MetaDataset
from torchmeta.datasets.utils import get_asset


class TCGA(MetaDataset):
    """
    The TCGA dataset [1]. A dataset of classification tasks over the values of
    an attribute, based on the gene expression data from patients diagnosed with
    specific types of cancer. This dataset is based on data from the Cancer
    Genome Atlas Program from the National Cancer Institute.

    Parameters
    ----------
    root : string
        Root directory where the dataset folder `omniglot` exists.

    meta_train : bool (default: `False`)
        Use the meta-train split of the dataset. If set to `True`, then the
        arguments `meta_val` and `meta_test` must be set to `False`. Exactly one 
        of these three arguments must be set to `True`.