def split(x, indices_or_sections, axis=0):
    """Split a tensor into chunks.

    Args:
        x: Input tensor.
        indices_or_sections: If an integer, N, the tensor will be split into N
            equal sections along `axis`. If a 1-D array of sorted integers,
            the entries indicate indices at which the tensor will be split
            along `axis`.
        axis: Axis along which to split. Defaults to `0`.

    Note:
        A split does not have to result in equal division when using
        Torch backend.

    Returns:
        A list of tensors.
    """
    if any_symbolic_tensors((x,)):
        return Split(indices_or_sections, axis=axis).symbolic_call(x)
    return backend.numpy.split(x, indices_or_sections, axis=axis)
Split a tensor into chunks.

    Args:
        x: Input tensor.
        indices_or_sections: If an integer, N, the tensor will be split into N
            equal sections along `axis`. If a 1-D array of sorted integers,
            the entries indicate indices at which the tensor will be split
            along `axis`.
        axis: Axis along which to split. Defaults to `0`.

    Note:
        A split does not have to result in equal division when using
        Torch backend.

    Returns:
        A list of tensors.
    
keras/src/ops/numpy.py