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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def sgd(fun, x0, data, args=(), bounds=None, batch_size=10, maxiter=5000, updater=None, eval_obj=False, random_state=None):
 """ Stochastic Gradient Descent. Parameters fun : callable the function to *minimize*, this must have the signature ``[obj,]`` ``sgd`` if an objective function value is going to be returned by ``fun``. x0 : ndarray a sequence/1D array of initial values for the parameters to learn. data : ndarray a numpy array or sequence of data to input into ``fun``. This will be split along the first axis (axis=0), and then input into ``fun``. args : sequence, optional an optional sequence of arguments to give to fun. bounds : sequence, optional Bounds for variables, (min, max) pairs for each element in x, defining the bounds on that parameter. Use None for one of min or max when there is no bound in that direction. batch_size : int, optional The number of observations in an SGD batch. maxiter : int, optional Number of mini-batch iterations before optimization terminates. updater : SGDUpdater, optional The type of gradient update to use, by default this is Adam. eval_obj : bool, optional This indicates whether or not ``fun`` also evaluates and returns the objective function value. If this is true, ``fun`` must return ``(obj, grad)`` and then a list of objective function values is also returned. random_state : int or RandomState, optional random seed Returns ------- res : OptimizeResult x : narray the final result norms : list the list of gradient norms message : str the convergence condition ('maxiter reached' or error) objs : list the list of objective function evaluations if ``eval_obj`` is True. fun : float the final objective function evaluation if ``eval_obj`` is True. """

    if updater is None:
        updater = Adam()

    # Make sure we aren't using a recycled updater
    updater.reset()

    N = _len_data(data)
    x = np.array(x0, copy=True, dtype=float)
    D = x.shape[0]

    # Make sure we have a valid batch size
    batch_size = min(batch_size, N)

    # Process bounds
    if bounds is not None:
        if len(bounds) != D:
            raise ValueError("The dimension of the bounds does not match x0!")

        lower, upper = zip(*map(normalize_bound, bounds))
        lower = np.array(lower)
        upper = np.array(upper)

    # Learning Records
    obj = None
    objs = []
    norms = []

    for batch in gen_batch(data, batch_size, maxiter, random_state):

        if not eval_obj:
            grad = fun(x, *chain(batch, args))
        else:
            obj, grad = fun(x, *chain(batch, args))
            objs.append(obj)

        norms.append(np.linalg.norm(grad))

        # Truncate gradients if bounded
        if bounds is not None:
            xlower = x <= lower
            grad[xlower] = np.minimum(grad[xlower], 0)
            xupper = x >= upper
            grad[xupper] = np.maximum(grad[xupper], 0)

        # perform update
        x = updater(x, grad)

        # Trucate steps if bounded
        if bounds is not None:
            x = np.clip(x, lower, upper)

    # Format results
    res = OptimizeResult(
        x=x,
        norms=norms,
        message='maxiter reached',
        fun=obj,
        objs=objs
    )

    return res