This change fixes some common test level errors.

1. Error type: Out-of-bound python value to numpy data type conversion
   Solutions:
       - If the test is not specifically designed to evaluate overflow
         behavior, modify the values or data types used to ensure they fall
         within the range for the target NumPy data type.
       - Or exclude or modify values that are incompatible with certain data
         types.
       - To retain the previous behavior where out-of-bounds conversions
         resulted in overflow, use np.array(value).astype(dtype) instead of
         np.array(value, dtype=dtype).

2. Error type: Deprecated NumPy namespaces/APIs
   Solution: Replace deprecated code with the recommended alternatives from
       the NumPy documentation. If the suggested replacement isn't available
       in NumPy 1.x, implement version-specific logic or macros to ensure
       compatibility across different NumPy versions.

3. Error type: NumPy 2.0 has updated requirements for the __array__ protocol. np.array(obj, copy=False) is no longer supported.
   Solution: Follow the NumPy 2.0 migration guide to address the error: https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword.

4. Error type: NumPy scalar representations now include a np.type prefix (e.g., np.int64(1) instead of just 1).
   Solutions:
       - Convert the NumPy scalar back to a Python value before printing or
         comparing it. Use methods like .item() or .tolist().
       - Or if it is an AssertionError, update the expected error messages to
         include the np.type prefix for NumPy values.

An additional error: The maximum number of dimensions (and arguments) was increased to 64 https://numpy.org/doc/stable/numpy_2_0_migration_guide.html#increased-maximum-number-of-dimensions. Update the maximum value in the tensor test TFETensorTest.testNumpyTooManyDimensions.

PiperOrigin-RevId: 663106876