class_docstr: The minibatch plugin accuracy metric.
  This metric only works at training time.
  
  This metric computes the average accuracy over patterns
  from a single minibatch.
  It reports the result after each iteration.
  
  If a more coarse-grained logging is needed, consider using
  :class:`EpochAccuracy` instead.
class_docstr_tok: ["The","minibatch","plugin","accuracy","metric",".","This","metric","only","works","at","training","time",".","This","metric","computes","the","average","accuracy","over","patterns","from","a","single","minibatch",".","It","reports","the","result","after","each","iteration",".","If","a","more","coarse-grained","logging","is","needed",",","consider","using",":","class",":","`","EpochAccuracy","`","instead","."]
docstr: Creates an instance of the MinibatchAccuracy metric.
docstr_tok: ["Creates","an","instance","of","the","MinibatchAccuracy","metric","."]
code: def __init__(self):
      """
      Creates an instance of the MinibatchAccuracy metric.
      """
      super(MinibatchAccuracy, self).__init__(
          reset_at="iteration", emit_at="iteration", mode="train"
      )
  
imports: from typing import List, Optional, Union, Dict
  import torch
  from torch import Tensor
  from avalanche.evaluation import Metric, GenericPluginMetric
  from avalanche.evaluation.metrics.mean import Mean
  from collections import defaultdict
label_desc: Use image node_id 1 to create a new MinibatchAccuracy object from inherited base classes: AccuracyPluginMetric with example: obj = MinibatchAccuracy()