I keep getting TensorFlow ValueError and I dont know what is causing it

So i installed everything to run a jupyter notebook in Visual studio code and have tensorflow and python fully updated. However, I keep running into an error when I run one of the Kernels. Specifically,

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\utils\traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)    119     filtered_tb = _process_traceback_frames(e.__traceback__)    120     # To get the full stack trace, call:    121     # `keras.config.disable_traceback_filtering()`--> 122     raise e.with_traceback(filtered_tb) from None    123 finally:    124     del filtered_tb

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\trainers\data_adapters\__init__.py:113, in get_data_adapter(x, y, sample_weight, batch_size, steps_per_epoch, shuffle, class_weight)    105     return GeneratorDataAdapter(x)    106     # TODO: should we warn or not?    107     # warnings.warn(    108     #     "`shuffle=True` was passed, but will be ignored since the "   (...)    111     # )    112 else:--> 113     raise ValueError(f"Unrecognized data type: x={x} (of type {type(x)})

at the end it says ValueError: unrecognized data type x=[10.0] (of type <class 'list'>)

Is there any way to fix this?

This is my current code:

import tensorflow as tfimport numpy as npfrom tensorflow import kerasmodel = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])model.compile(optimizer='sgd', loss='mean_squared_error')xs = np.array([-1.0,  0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)ys = np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0], dtype=float)model.fit(xs, ys, epochs=500)print(model.predict([10.0])) #This is where the error is

This is where the Value error is

if array_data_adapter.can_convert_arrays((x, y, sample_weight)):        return ArrayDataAdapter(            x,            y,            sample_weight=sample_weight,            class_weight=class_weight,            shuffle=shuffle,            batch_size=batch_size,            steps=steps_per_epoch,        )    elif is_tf_dataset(x):        # Unsupported args: y, sample_weight, shuffle        if y is not None:            raise_unsupported_arg("y", "the targets", "tf.data.Dataset")        if sample_weight is not None:            raise_unsupported_arg("sample_weights", "the sample weights", "tf.data.Dataset"            )        return TFDatasetAdapter(            x, class_weight=class_weight, distribution=distribution        )        # TODO: should we warn or not?        # warnings.warn(        #     "`shuffle=True` was passed, but will be ignored since the "        #     "data `x` was provided as a tf.data.Dataset. The Dataset is "        #     "expected to already be shuffled "        #     "(via `.shuffle(tf.data.AUTOTUNE)`)"        # )    elif isinstance(x, py_dataset_adapter.PyDataset):        if y is not None:            raise_unsupported_arg("y", "the targets", "PyDataset")        if sample_weight is not None:            raise_unsupported_arg("sample_weights", "the sample weights", "PyDataset"            )        return PyDatasetAdapter(x, class_weight=class_weight, shuffle=shuffle)    elif is_torch_dataloader(x):        if y is not None:            raise_unsupported_arg("y", "the targets", "torch DataLoader")        if sample_weight is not None:            raise_unsupported_arg("sample_weights", "the sample weights", "torch DataLoader"            )        if class_weight is not None:            raise ValueError("Argument `class_weight` is not supported for torch "                f"DataLoader inputs. Received: class_weight={class_weight}"            )        return TorchDataLoaderAdapter(x)        # TODO: should we warn or not?        # warnings.warn(        #     "`shuffle=True` was passed, but will be ignored since the "        #     "data `x` was provided as a torch DataLoader. The DataLoader "        #     "is expected to already be shuffled."        # )    elif isinstance(x, types.GeneratorType):        if y is not None:            raise_unsupported_arg("y", "the targets", "PyDataset")        if sample_weight is not None:            raise_unsupported_arg("sample_weights", "the sample weights", "PyDataset"            )        if class_weight is not None:            raise ValueError("Argument `class_weight` is not supported for Python "                f"generator inputs. Received: class_weight={class_weight}"            )        return GeneratorDataAdapter(x)        # TODO: should we warn or not?        # warnings.warn(        #     "`shuffle=True` was passed, but will be ignored since the "        #     "data `x` was provided as a generator. The generator "        #     "is expected to yield already-shuffled data."        # )    else:        raise ValueError(f"Unrecognized data type: x={x} (of type {type(x)})")

This is another segment of code that is highlighted

def filter_traceback(fn):"""Filter out Keras-internal traceback frames in exceptions raised by fn."""    @wraps(fn)    def error_handler(*args, **kwargs):        if not is_traceback_filtering_enabled():            return fn(*args, **kwargs)        filtered_tb = None        try:            return fn(*args, **kwargs)        except Exception as e:            filtered_tb = _process_traceback_frames(e.__traceback__)            # To get the full stack trace, call:            # `keras.config.disable_traceback_filtering()`            raise e.with_traceback(filtered_tb) from None        finally:            del filtered_tb