class_docstr: The default accuracy metric loads in a different accuracy metric depending on the loss function or criterion in
  use at the start of training. Default for keys: `acc`, `accuracy`. The following bindings are in place for both nn
  and functional variants:
  
  - cross entropy loss -> :class:`.CategoricalAccuracy` [DEFAULT]
  - nll loss -> :class:`.CategoricalAccuracy`
  - mse loss -> :class:`.MeanSquaredError`
  - bce loss -> :class:`.BinaryAccuracy`
  - bce loss with logits -> :class:`.BinaryAccuracy`
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code: def __init__(self):
      super(DefaultAccuracy, self).__init__(
          "placeholder"
      )  # Don't set yet, wait for reset
  
      self.metric = (
          CategoricalAccuracy()
      )  # Default to CategoricalAccuracy
      self.name = self.metric.name
      self._loaded = False
      self._train = True
  
file_path: torchbearer/torchbearer/metrics/default.py
imports: import torch.nn
  import torch.nn.functional
  import torchbearer
  from torchbearer.metrics import default_for_key, Metric, CategoricalAccuracy, MeanSquaredError, BinaryAccuracy
label_desc: Use image node_id 1 to create a new DefaultAccuracy object from inherited base classes: Metric with example: obj = DefaultAccuracy()