How to use the hub.collections.tensor.from_array function in hub

)
    parser.add_argument(
        "-o", "--output_name", type=str, help="Dataset output name", default="mnist",
    )
    args = parser.parse_args()
    files = ["training", "testing"]
    dicts = []
    for f in files:
        images, labels = load_mnist(f, path=args.dataset_path)
        dicts += [{"images": images, "labels": labels}]
    images = np.concatenate([d["images"] for d in dicts])
    # images = images.reshape((len(images), 3, 32, 32))
    labels = np.concatenate([np.array(d["labels"], dtype="int8") for d in dicts])
    print(images.shape, labels.shape)
    # Image.fromarray(images[1000]).save("./data/image.png")
    images_t = tensor.from_array(images)
    labels_t = tensor.from_array(labels)
    # coarse_labels_t = tensor.from_array(coarse_labels)
    ds = dataset.from_tensors({"data": images_t, "labels": labels_t})
    ds.store(f"./data/generated/{args.output_name}")

parser.add_argument(
        "-o", "--output_name", type=str, help="Dataset output name", default="mnist",
    )
    args = parser.parse_args()
    files = ["training", "testing"]
    dicts = []
    for f in files:
        images, labels = load_mnist(f, path=args.dataset_path)
        dicts += [{"images": images, "labels": labels}]
    images = np.concatenate([d["images"] for d in dicts])
    # images = images.reshape((len(images), 3, 32, 32))
    labels = np.concatenate([np.array(d["labels"], dtype="int8") for d in dicts])
    print(images.shape, labels.shape)
    # Image.fromarray(images[1000]).save("./data/image.png")
    images_t = tensor.from_array(images)
    labels_t = tensor.from_array(labels)
    # coarse_labels_t = tensor.from_array(coarse_labels)
    ds = dataset.from_tensors({"data": images_t, "labels": labels_t})
    ds.store(f"./data/generated/{args.output_name}")

help="Dataset output name",
        default="cifar10",
    )
    args = parser.parse_args()
    files = sorted([f for f in os.listdir(args.dataset_path) if "_batch" in f])
    dicts = []
    for f in files:
        with open(os.path.join(args.dataset_path, f), "rb") as fh:
            dicts += [pickle.load(fh, encoding="bytes")]
            print(dicts[-1].keys())
    images = np.concatenate([d[b"data"] for d in dicts])
    images = images.reshape((len(images), 3, 32, 32))
    labels = np.concatenate([np.array(d[b"labels"], dtype="int16") for d in dicts])
    print(images.shape, labels.shape)
    Image.fromarray(images[1000].transpose(1, 2, 0)).save("./data/image.png")
    images_t = tensor.from_array(images)
    labels_t = tensor.from_array(labels)
    ds = dataset.from_tensors({"data": images_t, "labels": labels_t})
    ds.store(f"./data/generated/{args.output_name}")

dicts += [pickle.load(fh, encoding="bytes")]
            print(dicts[-1].keys())
    images = np.concatenate([d[b"data"] for d in dicts])
    images = images.reshape((len(images), 3, 32, 32))
    fine_labels = np.concatenate(
        [np.array(d[b"fine_labels"], dtype="int16") for d in dicts]
    )
    coarse_labels = np.concatenate(
        [np.array(d[b"coarse_labels"], dtype="int16") for d in dicts]
    )
    print(images.shape, fine_labels.shape, coarse_labels.shape)
    Image.fromarray(images[1000].transpose(1, 2, 0)).save("./data/image.png")

    images_t = tensor.from_array(images)
    fine_labels_t = tensor.from_array(fine_labels)
    coarse_labels_t = tensor.from_array(coarse_labels)
    ds = dataset.from_tensors(
        {
            "data": images_t,
            "fine_labels": fine_labels_t,
            "coarse_labels": coarse_labels_t,
        }
    )
    ds.store(f"./data/generated/{args.output_name}")

default="cifar10",
    )
    args = parser.parse_args()
    files = sorted([f for f in os.listdir(args.dataset_path) if "_batch" in f])
    dicts = []
    for f in files:
        with open(os.path.join(args.dataset_path, f), "rb") as fh:
            dicts += [pickle.load(fh, encoding="bytes")]
            print(dicts[-1].keys())
    images = np.concatenate([d[b"data"] for d in dicts])
    images = images.reshape((len(images), 3, 32, 32))
    labels = np.concatenate([np.array(d[b"labels"], dtype="int16") for d in dicts])
    print(images.shape, labels.shape)
    Image.fromarray(images[1000].transpose(1, 2, 0)).save("./data/image.png")
    images_t = tensor.from_array(images)
    labels_t = tensor.from_array(labels)
    ds = dataset.from_tensors({"data": images_t, "labels": labels_t})
    ds.store(f"./data/generated/{args.output_name}")

for f in files:
        with open(os.path.join(args.dataset_path, f), "rb") as fh:
            dicts += [pickle.load(fh, encoding="bytes")]
            print(dicts[-1].keys())
    images = np.concatenate([d[b"data"] for d in dicts])
    images = images.reshape((len(images), 3, 32, 32))
    fine_labels = np.concatenate(
        [np.array(d[b"fine_labels"], dtype="int16") for d in dicts]
    )
    coarse_labels = np.concatenate(
        [np.array(d[b"coarse_labels"], dtype="int16") for d in dicts]
    )
    print(images.shape, fine_labels.shape, coarse_labels.shape)
    Image.fromarray(images[1000].transpose(1, 2, 0)).save("./data/image.png")

    images_t = tensor.from_array(images)
    fine_labels_t = tensor.from_array(fine_labels)
    coarse_labels_t = tensor.from_array(coarse_labels)
    ds = dataset.from_tensors(
        {
            "data": images_t,
            "fine_labels": fine_labels_t,
            "coarse_labels": coarse_labels_t,
        }
    )
    ds.store(f"./data/generated/{args.output_name}")

with open(os.path.join(args.dataset_path, f), "rb") as fh:
            dicts += [pickle.load(fh, encoding="bytes")]
            print(dicts[-1].keys())
    images = np.concatenate([d[b"data"] for d in dicts])
    images = images.reshape((len(images), 3, 32, 32))
    fine_labels = np.concatenate(
        [np.array(d[b"fine_labels"], dtype="int16") for d in dicts]
    )
    coarse_labels = np.concatenate(
        [np.array(d[b"coarse_labels"], dtype="int16") for d in dicts]
    )
    print(images.shape, fine_labels.shape, coarse_labels.shape)
    Image.fromarray(images[1000].transpose(1, 2, 0)).save("./data/image.png")

    images_t = tensor.from_array(images)
    fine_labels_t = tensor.from_array(fine_labels)
    coarse_labels_t = tensor.from_array(coarse_labels)
    ds = dataset.from_tensors(
        {
            "data": images_t,
            "fine_labels": fine_labels_t,
            "coarse_labels": coarse_labels_t,
        }
    )
    ds.store(f"./data/generated/{args.output_name}")