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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 run( self, inputs: Dict[str, Union[float, Iterable]], torch_size: Optional[int] = None, ) -> Union[float, Iterable]: """Executes the GrFN over a particular set of inputs and returns the result. Args: inputs: Input set where keys are the names of input nodes in the GrFN and each key points to a set of input values (or just one). Returns: A set of outputs from executing the GrFN, one for every set of inputs. """

        # Set input values
        for i in self.inputs:
            self.nodes[i]["value"] = inputs[i]

        for func_set in self.function_sets:
            for func_name in func_set:
                lambda_fn = self.nodes[func_name]["lambda_fn"]
                output_node = list(self.successors(func_name))[0]

                signature = self.nodes[func_name]["func_inputs"]
                input_values = [self.nodes[n]["value"] for n in signature]
                res = lambda_fn(*input_values)

                if torch_size is not None and len(signature) == 0:
                    self.nodes[output_node]["value"] = torch.tensor(
                        [res] * torch_size, dtype=torch.double
                    )
                else:
                    self.nodes[output_node]["value"] = res

        # Return the output
        return self.nodes[self.output_node]["value"]