def flow(self, x, y=None, batch_size=32, shuffle=True, sample_weight=None, seed=None, save_to_dir=None, save_prefix='', save_format='png', subset=None):
    'Takes numpy data & label arrays, and generates batches of augmented data.\n\n        # Arguments\n            x: Input data. Numpy array of rank 4 or a tuple.\n                If tuple, the first element\n                should contain the images and the second element\n                another numpy array or a list of numpy arrays\n                that gets passed to the output\n                without any modifications.\n                Can be used to feed the model miscellaneous data\n                along with the images.\n                In case of grayscale data, the channels axis of the image array\n                should have value 1, and in case\n                of RGB data, it should have value 3.\n            y: Labels.\n            batch_size: Int (default: 32).\n            shuffle: Boolean (default: True).\n            sample_weight: Sample weights.\n            seed: Int (default: None).\n            save_to_dir: None or str (default: None).\n                This allows you to optionally specify a directory\n                to which to save the augmented pictures being generated\n                (useful for visualizing what you are doing).\n            save_prefix: Str (default: `\'\'`).\n                Prefix to use for filenames of saved pictures\n                (only relevant if `save_to_dir` is set).\n                save_format: one of "png", "jpeg"\n                (only relevant if `save_to_dir` is set). Default: "png".\n            subset: Subset of data (`"training"` or `"validation"`) if\n                `validation_split` is set in `ImageDataGenerator`.\n\n        # Returns\n            An `Iterator` yielding tuples of `(x, y)`\n                where `x` is a numpy array of image data\n                (in the case of a single image input) or a list\n                of numpy arrays (in the case with\n                additional inputs) and `y` is a numpy array\n                of corresponding labels. If \'sample_weight\' is not None,\n                the yielded tuples are of the form `(x, y, sample_weight)`.\n                If `y` is None, only the numpy array `x` is returned.\n        '
    return NumpyArrayIterator(x, y, self, batch_size=batch_size, shuffle=shuffle, sample_weight=sample_weight, seed=seed, data_format=self.data_format, save_to_dir=save_to_dir, save_prefix=save_prefix, save_format=save_format, subset=subset)
Takes numpy data & label arrays, and generates batches of augmented data.

# Arguments
    x: Input data. Numpy array of rank 4 or a tuple.
        If tuple, the first element
        should contain the images and the second element
        another numpy array or a list of numpy arrays
        that gets passed to the output
        without any modifications.
        Can be used to feed the model miscellaneous data
        along with the images.
        In case of grayscale data, the channels axis of the image array
        should have value 1, and in case
        of RGB data, it should have value 3.
    y: Labels.
    batch_size: Int (default: 32).
    shuffle: Boolean (default: True).
    sample_weight: Sample weights.
    seed: Int (default: None).
    save_to_dir: None or str (default: None).
        This allows you to optionally specify a directory
        to which to save the augmented pictures being generated
        (useful for visualizing what you are doing).
    save_prefix: Str (default: `''`).
        Prefix to use for filenames of saved pictures
        (only relevant if `save_to_dir` is set).
        save_format: one of "png", "jpeg"
        (only relevant if `save_to_dir` is set). Default: "png".
    subset: Subset of data (`"training"` or `"validation"`) if
        `validation_split` is set in `ImageDataGenerator`.

# Returns
    An `Iterator` yielding tuples of `(x, y)`
        where `x` is a numpy array of image data
        (in the case of a single image input) or a list
        of numpy arrays (in the case with
        additional inputs) and `y` is a numpy array
        of corresponding labels. If 'sample_weight' is not None,
        the yielded tuples are of the form `(x, y, sample_weight)`.
        If `y` is None, only the numpy array `x` is returned.
def flow(self, x, y=None, batch_size=32, shuffle=True, sample_weight=None, seed=None, save_to_dir=None, save_prefix=, save_format='png', subset=None):
    'Takes numpy data & label arrays, and generates batches of augmented data.\n\n        # Arguments\n            x: Input data. Numpy array of rank 4 or a tuple.\n                If tuple, the first element\n                should contain the images and the second element\n                another numpy array or a list of numpy arrays\n                that gets passed to the output\n                without any modifications.\n                Can be used to feed the model miscellaneous data\n                along with the images.\n                In case of grayscale data, the channels axis of the image array\n                should have value 1, and in case\n                of RGB data, it should have value 3.\n            y: Labels.\n            batch_size: Int (default: 32).\n            shuffle: Boolean (default: True).\n            sample_weight: Sample weights.\n            seed: Int (default: None).\n            save_to_dir: None or str (default: None).\n                This allows you to optionally specify a directory\n                to which to save the augmented pictures being generated\n                (useful for visualizing what you are doing).\n            save_prefix: Str (default: `\'\'`).\n                Prefix to use for filenames of saved pictures\n                (only relevant if `save_to_dir` is set).\n                save_format: one of "png", "jpeg"\n                (only relevant if `save_to_dir` is set). Default: "png".\n            subset: Subset of data (`"training"` or `"validation"`) if\n                `validation_split` is set in `ImageDataGenerator`.\n\n        # Returns\n            An `Iterator` yielding tuples of `(x, y)`\n                where `x` is a numpy array of image data\n                (in the case of a single image input) or a list\n                of numpy arrays (in the case with\n                additional inputs) and `y` is a numpy array\n                of corresponding labels. If \'sample_weight\' is not None,\n                the yielded tuples are of the form `(x, y, sample_weight)`.\n                If `y` is None, only the numpy array `x` is returned.\n        '
    return NumpyArrayIterator(x, y, self, batch_size=batch_size, shuffle=shuffle, sample_weight=sample_weight, seed=seed, data_format=self.data_format, save_to_dir=save_to_dir, save_prefix=save_prefix, save_format=save_format, subset=subset)