class_docstr: Object to simulate MachAr instance 
code: def __init__(self, ftype, **kwargs):
      params = _MACHAR_PARAMS[ftype]
      float_conv = lambda v: array([v], ftype)
      float_to_float = lambda v: _fr1(float_conv(v))
      float_to_str = lambda v: (
          params["fmt"] % array(_fr0(v)[0], ftype)
      )
  
      self.title = params["title"]
      # Parameter types same as for discovered MachAr object.
      self.epsilon = self.eps = float_to_float(kwargs.pop("eps"))
      self.epsneg = float_to_float(kwargs.pop("epsneg"))
      self.xmax = self.huge = float_to_float(kwargs.pop("huge"))
      self.xmin = self.tiny = float_to_float(kwargs.pop("tiny"))
      self.ibeta = params["itype"](kwargs.pop("ibeta"))
      self.__dict__.update(kwargs)
      self.precision = int(-log10(self.eps))
      self.resolution = float_to_float(
          float_conv(10) ** (-self.precision)
      )
      self._str_eps = float_to_str(self.eps)
      self._str_epsneg = float_to_str(self.epsneg)
      self._str_xmin = float_to_str(self.xmin)
      self._str_xmax = float_to_str(self.xmax)
      self._str_resolution = float_to_str(self.resolution)
  
imports: from __future__ import division, absolute_import, print_function
  import warnings
  from .machar import MachAr
  from .overrides import set_module
  from .None import numeric
  from .None import numerictypes
  from .numeric import array, inf
  from .umath import log10, exp2
  from .None import umath
label_desc: Use image node_id 1 to create a new MachArLike object from inherited base classes: object with example: obj = MachArLike(ftype)