code: def _is_metric_diffable(metric_value: Any):
      """Check whether a metric value is a number or an ndarray of numbers."""
      return isinstance(
          metric_value, (numbers.Number, types.StructuredMetricValue)
      ) or (
          isinstance(metric_value, np.ndarray)
          and np.issubdtype(metric_value.dtype, np.number)
      )
  
imports: import copy
  import datetime
  import itertools
  import numbers
  from typing import Any, Dict, Iterable, Iterator, List, NamedTuple, Optional, Tuple, Type, TypeVar, Union
  import apache_beam
  import numpy
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.evaluators import counter_util
  from tensorflow_model_analysis.evaluators import eval_saved_model_util
  from tensorflow_model_analysis.evaluators import evaluator
  from tensorflow_model_analysis.evaluators import jackknife
  from tensorflow_model_analysis.evaluators import keras_util
  from tensorflow_model_analysis.evaluators import metrics_validator
  from tensorflow_model_analysis.evaluators import poisson_bootstrap
  from tensorflow_model_analysis.extractors import slice_key_extractor
  from tensorflow_model_analysis.metrics import metric_specs
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.slicer import slicer_lib
  from tensorflow_model_analysis.utils import model_util
  from tensorflow_model_analysis.utils import util
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 7 for calling a global function with example usage: _is_metric_diffable(metric_value) and returns: unknown