human
Solve the following python programming problem given the following code:
def _populate_grad_dict(var_to_app_to_idx, grad_dict, wrt, cost_name = None) : 
    "
        Helper function for grad function.

        var_to_app_to_idx: a dictionary mapping a variable to
                a second dictionary.
                the second dictionary maps apply nodes acting on
                this variable to the variable's index in the apply
                node's input list

        grad_dict: A dictionary mapping variables to their gradients.
                   Should be populated by grad function, which should:
                       -Set the gradient with respect to the cost to 1
                       -Load all gradients from known_grads, possibly
                        overriding the cost
                       -Set the gradient for disconnected
                        inputs to a variable with type DisconnectedType()

        wrt: the minimal set of variables that must be included in grad_dict

        cost_name: The name of the cost being differentiated, optional.
                    used to name the grad with respect to x as
                    (d<cost_name>/dx)

        returns: a list of gradients corresponding to wrt

    " 
    term_dict = OrderedDict () 
    def access_term_cache(node) : 
        ' Populates term_dict[node] and returns it ' 
        if (node not in term_dict) : 
            inputs = node.inputs 
            output_grads = [access_grad_cache (var) for var in node.outputs] 
            outputs_connected = [(not isinstance (g.type, DisconnectedType)) for g in output_grads] 
            connection_pattern = _node_to_pattern (node) 
            inputs_connected = [(True in [(input_to_output and output_to_cost) for (input_to_output, output_to_cost) in zip (input_to_outputs, outputs_connected)]) for input_to_outputs in connection_pattern] 
            output_is_int = [(hasattr (output.type, 'dtype') and (output.type.dtype in theano.tensor.discrete_dtypes)) for output in node.outputs] 
            ograd_is_nan = [isinstance (output.type, NullType) for output in...

    
    

    Fix the buggy line: rval = [access_grad_cache (access_term_cache) for elem in wrt]
assistant
#Here is Python program to fix the buggy line: rval = [access_grad_cache (access_term_cache) for elem in wrt]
.

rval = [access_grad_cache (elem) for elem in wrt]