Casted finfo attributes from np.float to float for Array API compliance

[vfdev-5]

Description:

• Addresses "jnp.finfo: Update eps, max, min, smallest_normal attributes to Python floats" from Remaining API changes for array API compliance #21088 (https://data-apis.org/array-api/latest/API_specification/generated/array_api.finfo.html#finfo)

>>> import jax.numpy as jnp
>>> f = jnp.finfo("float16")
>>> f.__dict__

Output on main:

{
  'dtype': dtype('float16'), 'precision': 3, 'iexp': 5, 'maxexp': 16, 
  'minexp': -14, 'negep': -11, 'machep': -10, 'resolution': np.float16(0.001), 
  'epsneg': np.float16(0.0004883), 'smallest_subnormal': np.float16(6e-08), 'bits': 16, 'max': np.float16(65500.0), 
  'min': np.float16(-65500.0), 'eps': np.float16(0.000977), 'nexp': 5, 'nmant': 10, '_machar': <numpy._core.getlimits.MachArLike object at 0x7f4b3bdaff70>, 
  '_str_tiny': '6.10352e-05', '_str_max': '6.55040e+04', '_str_epsneg': '4.88281e-04', '_str_eps': '9.76562e-04',
  '_str_resolution': '1.00040e-03', '_str_smallest_normal': '6.10352e-05', '_str_smallest_subnormal': '5.96046e-08'
}

This PR:

{
  'dtype': dtype('float16'), 'precision': 3, 'iexp': 5, 'maxexp': 16, 
  'minexp': -14, 'negep': -11, 'machep': -10, 'resolution': 0.0010004043579101562, 
  'epsneg': 0.00048828125, 'smallest_subnormal': 5.960464477539063e-08, 'bits': 16, 'max': 65504.0, 
  'min': -65504.0, 'eps': 0.0009765625, 'nexp': 5, 'nmant': 10, '_machar': <numpy._core.getlimits.MachArLike object at 0x7fd87f65aa10>, 
  '_str_tiny': '6.10352e-05', '_str_max': '6.55040e+04', '_str_epsneg': '4.88281e-04', '_str_eps': '9.76562e-04',
  '_str_resolution': '1.00040e-03', '_str_smallest_normal': '6.10352e-05', '_str_smallest_subnormal': '5.96046e-08'
}

[jakevdp]

I think the better fix here would be to change this at the source, in ml_dtypes. The only reason it returns scalar typed values rather than built-in floats is because it was following the precedent in NumPy; presumably NumPy is changing, so we should change ml_dtypes as well.

[vfdev-5]

I was pointed out to this issue: data-apis/array-api#405 where essentially

numpy has support for extended precision types, so python float doesn't work for longdouble

Source: numpy/numpy#26523

It means that most probably numpy will keep eps, max, min, smallest_normal attributes as np.floating and array-API will add additional note on returned types.
@jakevdp @Micky774 so, we may want to revert changes in this PR and update jax/experimental/array_api/_data_type_functions.py to return np.floats as numpy ?

[jakevdp]

Yeah in that case let's not make any changes.