class_docstr: The minibatch plugin class 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:`EpochClassAccuracy` instead.
docstr: Creates an instance of the MinibatchClassAccuracy metric.
docstr_tok: ["Creates","an","instance","of","the","MinibatchClassAccuracy","metric","."]
code: def __init__(self, classes=None):
      """
      Creates an instance of the MinibatchClassAccuracy metric.
      """
      super().__init__(
          reset_at="iteration",
          emit_at="iteration",
          mode="train",
          classes=classes,
      )
  
imports: from typing import Dict, List, Set, Union, TYPE_CHECKING, Iterable, Optional
  from collections import defaultdict, OrderedDict
  import torch
  from torch import Tensor
  from avalanche.evaluation import Metric, PluginMetric, _ExtendedGenericPluginMetric, _ExtendedPluginMetricValue
  from avalanche.evaluation.metric_utils import default_metric_name_template, generic_get_metric_name
  from avalanche.evaluation.metrics import Mean
label_desc: Use image node_id 1 to create a new MinibatchClassAccuracy object from inherited base classes: ClassAccuracyPluginMetric with example: obj = MinibatchClassAccuracy(classes)