human
Solve the following python programming problem given the following code:
def __init__(self, f, pt, eps = None, out_type = None) : 
    "Return the gradient of f at pt.

        :param f: a differentiable function such that f(*pt) is a scalar
        :param pt: an ndarray, a list of ndarrays or tuple of ndarrays
        :param out_type: dtype of output, if complex (i.e. 'complex32' or
        'complex64')
        This function computes the gradient by a one-sided finite
        differences of a fixed step size (eps).

        It is assumed that f(...) will return a scalar.
        It is assumed that all f's inputs are numpy.ndarray objects.

        :param eps: the stepsize for the finite differencing.  None means
          input dtype-dependent. See `type_eps`.
        " 
    def prod(inputs) : 
        rval = 1 
        for i in inputs : 
            rval *= i 
return rval 
packed_pt = False 
    if (not isinstance (pt, (list, tuple))) : 
        pt = [pt] 
        packed_pt = True 
apt = [numpy.array (p) for p in pt] 
    shapes = [p.shape for p in apt] 
    dtypes = [str (p.dtype) for p in apt] 
    total_size = builtins.sum ((prod (sh) for sh in shapes)) 
    working_dtype = builtins.min (((self.type_eps [dt], dt) for dt in dtypes)) [1] 
    x = numpy.ndarray ((eps,), dtype = working_dtype) 
    if ((out_type is not None) and out_type.startswith ('complex')) : 
        gx = numpy.ndarray ((total_size,), dtype = out_type) 
else : 
        gx = numpy.ndarray ((total_size,), dtype = working_dtype) 
if (eps is None) : 
        eps = builtins.max ((self.type_eps [dt] for dt in dtypes)) 
cur_pos = 0 
    self.gf = [] 
    for (i, p) in enumerate (apt) : 
        p_size = prod (p.shape) 
        apt [i] = x [cur_pos : (cur_pos + p_size)].reshape (p.shape) 
        self.gf.append (gx [cur_pos : (cur_pos + p_size)].reshape (p.shape)) 
        apt [i] [...] = p 
        cur_pos += p_size 
f_x = f (* [p.copy () for p in apt]) 
    x_copy = x.copy () 
    for i in xrange (total_size) : 
        x [:] = x_copy 
        x [i] += eps 
        f_eps = f (* apt) 
        gx [i] = ((f_eps -...

    
    

    Fix the buggy line: x = numpy.ndarray ((eps,), dtype = working_dtype)
assistant
#Here is Python program to fix the buggy line: x = numpy.

x = numpy.ndarray ((total_size,), dtype = working_dtype)