class_docstr: A object which creates evaluation metrics for Subnetworks.
class_docstr_tok: ["A","object","which","creates","evaluation","metrics","for","Subnetworks","."]
docstr: Creates a _SubnetworkMetrics.
  
  Args:
    use_tpu: Whether to use TPU-specific variable sharing logic. This ensures
      that eval metrics created on TPU can be written to disk on the host CPU.
  
  Returns:
    A `_SubnetworkMetrics` instance.
docstr_tok: ["Creates","a","_SubnetworkMetrics",".","Args",":","use_tpu",":","Whether","to","use","TPU-specific","variable","sharing","logic",".","This","ensures","that","eval","metrics","created","on","TPU","can","be","written","to","disk","on","the","host","CPU",".","Returns",":","A","`","_SubnetworkMetrics","`","instance","."]
code: def __init__(self, use_tpu=False):
      """Creates a _SubnetworkMetrics.
  
      Args:
        use_tpu: Whether to use TPU-specific variable sharing logic. This ensures
          that eval metrics created on TPU can be written to disk on the host CPU.
  
      Returns:
        A `_SubnetworkMetrics` instance.
      """
  
      self._use_tpu = use_tpu
      self._eval_metrics_store = _EvalMetricsStore()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import inspect
  from absl import logging
  from adanet import tf_compat
  import six
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new _SubnetworkMetrics object from inherited base classes: object with example: obj = _SubnetworkMetrics(use_tpu)