class Node:
    """
    Node class represents a node in the graph with a unique identifier and a set of edges to other nodes.
    """
    def __init__(self, name):
        """
        Initializes a new instance of the Node class.

        Args:
            name (str): The unique identifier of the node.
        """
        self.name = name
        self.edges = {}

    def add_edge(self, node, cost):
        """
        Adds an edge from this node to another node with an associated cost.

        Args:
            node (Node): The node that the edge connects to.
            cost (float): The cost of traversing the edge.
        """
        self.edges[node] = cost

    def get_cost(self, node):
        """
        Gets the cost of the edge between this node and another node.

        Args:
            node (Node): The node that the edge connects to.
        
        Returns:
            float: The cost of the edge. Returns infinity if no direct edge exists.
        """
        return self.edges.get(node, float('inf'))


class Graph:
    """
    Graph class represents a graph data structure with nodes and the edges between them.
    """
    def __init__(self):
        """
        Initializes a new instance of the Graph class.
        """
        self.nodes = {}

    def add_node(self, name):
        """
        Adds a node to the graph.

        Args:
            name (str): The unique identifier of the node.
        """
        self.nodes[name] = Node(name)

    def add_edge(self, node_name1, node_name2, cost):
        """
        Adds an edge between two nodes in the graph with an associated cost.

        Args:
            node_name1 (str): The unique identifier of the first node.
            node_name2 (str): The unique identifier of the second node.
            cost (float): The cost of traversing the edge.
        """
        self.nodes[node_name1].add_edge(self.nodes[node_name2], cost)
        self.nodes[node_name2].add_edge(self.nodes[node_name1], cost)  # For undirected graph

    def get_cost(self, node_name1, node_name2):
        """
        Gets the cost of the edge between two nodes.

        Args:
            node_name1 (str): The unique identifier of the first node.
            node_name2 (str): The unique identifier of the second node.
        
        Returns:
            float: The cost of the edge. Returns infinity if no direct edge exists.
        """
        return self.nodes[node_name1].get_cost(self.nodes[node_name2])   

# Implementing multi-goal path finding with dynamic graph heuristics and other functionalities
# would require understanding the specific requirements and constraints of the project, then generating the necessary code.

# Following are dummy classes to simulate an agent that generates and evaluates code and its tests:

import unittest

class CodeGenerationAgent:
    """
    The CodeGenerationAgent class simulates the generation of code by an AI.
    """

    def generate_code(self, prompt: str) -> str:
        """
        Simulates the generation of code based on a given prompt.
        Returns a dummy string.
        """
        return "def generated_function(x): return x * 2"

    def generate_tests(self, function_name: str) -> unittest.TestCase:
        """
        Simulates the generation of unit tests for a given function name.
        Returns a dummy TestCase class.
        """
        class GeneratedTests(unittest.TestCase):
            def test_func(self):
                self.assertEqual(eval(function_name)(5), 10)

        return GeneratedTests

    def evaluate_code(self, code: str) -> callable:
        """
        Simulates the evaluation of a generated code (string) to its function form.
        """
        return eval(code)

    def evaluate_tests(self, tests: unittest.TestCase) -> bool:
        """
        Simulates the evaluation of the generated test case.
        Returns True if the test passes, False otherwise.
        """
        suite = unittest.TestLoader().loadTestsFromTestCase(tests)
        test_result = unittest.TextTestRunner().run(suite)
        return test_result.wasSuccessful()

# An instance of the agent
agent = CodeGenerationAgent()

# Using the agent to generate code and evaluate it
generated_code = agent.generate_code("Generate me code to double a number.")
function_under_test = agent.evaluate_code(generated_code)

generated_tests = agent.generate_tests('function_under_test')

test_passes = agent.evaluate_tests(generated_tests)

print("The generated code is: ", generated_code)
print("Does the test pass? ", test_passes)

# Note: This is a proof-of-concept and doesn't actually generate useful code or tests.
# A real-life version of this would have to interact with an AI model to generate the code and tests. 
