Add tests for random_symbol_circuit_resolver_batch and random_circuit_resolver_batch

[mhucka]

This adds unit tests to tensorflow_quantum/python/util_test.py to verify the output shapes and types of random_symbol_circuit_resolver_batch and random_circuit_resolver_batch. These tests ensure that the batch generators return the correct number of cirq.Circuit and cirq.ParamResolver objects, and that symbols are correctly present in the resolvers when applicable.

[gemini-code-assist]

Code Review

This pull request adds unit tests for random_circuit_resolver_batch and random_symbol_circuit_resolver_batch in util_test.py. The review feedback suggests improving the robustness of these tests by verifying that the generated circuits are non-empty and contain the expected symbols.

[gemini-code-assist]

This test can be made more robust by also verifying that the generated circuits are not empty and do not contain any symbols, which is part of the function's contract.

Suggested change

	for circuit in circuits:
	self.assertIsInstance(circuit, cirq.Circuit)
	for circuit in circuits:
	self.assertIsInstance(circuit, cirq.Circuit)
	self.assertGreater(len(circuit), 0, "Generated circuit should not be empty.")
	self.assertEqual(len(util.get_circuit_symbols(circuit)), 0, "Circuit should not have symbols.")

[gemini-code-assist]

This test should also verify that the generated circuits contain the expected symbols. This is a key part of the function's contract, and the current test only checks the type of the circuit.

Suggested change

	for circuit in circuits:
	self.assertIsInstance(circuit, cirq.Circuit)
	for circuit in circuits:
	self.assertIsInstance(circuit, cirq.Circuit)
	extracted_symbols = util.get_circuit_symbols(circuit)
	expected_symbols = sorted([str(s) for s in symbols])
	self.assertListEqual(expected_symbols, sorted(extracted_symbols))