class_docstr: Utility class to calculate and hold metrics between
  two model outputs
class_docstr_tok: ["Utility","class","to","calculate","and","hold","metrics","between","two","model","outputs"]
code: def __init__(self, name, type):
      self.name = name
      self._metrics = []
  
      if type == "stringType":
          self._metrics.append(TopKMetrics(topk=1))
  
      elif type == "dictionaryType":
          self._metrics.append(TopKMetrics(topk=5))
  
      elif type == "imageType" or type == "multiArrayType":
          self._metrics.append(NoiseMetrics())
  
      else:
          raise Exception(
              """Unable to determine which metric to
              compute for output: {}""".format(
                  name
              )
          )
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import numpy
  from sys import stdout
  from os import listdir
  from six import string_types
  from .optimization_utils import _optimize_nn
  from coremltools.models import _SUPPORTED_QUANTIZATION_MODES, _QUANTIZATION_MODE_DEQUANTIZE, _QUANTIZATION_MODE_LOOKUP_TABLE_LINEAR, _QUANTIZATION_MODE_LOOKUP_TABLE_KMEANS, _QUANTIZATION_MODE_CUSTOM_LOOKUP_TABLE, _QUANTIZATION_MODE_LINEAR_QUANTIZATION, _QUANTIZATION_MODE_LINEAR_SYMMETRIC, _LUT_BASED_QUANTIZATION
  from ..utils import _get_nn_layers, _wp_to_fp16wp, _get_model, _macos_version
  from ..._deps import _HAS_SKLEARN
  from ...None import _MINIMUM_QUANTIZED_MODEL_SPEC_VERSION, _MINIMUM_FP16_SPEC_VERSION, _SPECIFICATION_VERSION_IOS_14
label_desc: Use image node_id 1 to create a new OutputMetric object  with example: obj = OutputMetric(name, type)