class_docstr: The ConsolePrinter callback simply outputs the training metrics to the console.
  
  Example: ::
  
      >>> import torch.nn
      >>> from torchbearer import Trial
      >>> from torchbearer.callbacks import ConsolePrinter
  
      # Example Trial which forgoes the usual printer for a console printer
      >>> printer = ConsolePrinter()
      >>> trial = Trial(None, callbacks=[printer], verbose=0).for_steps(1).run()
      0/1(t):
  
  Args:
      validation_label_letter (str): This is the letter displayed after the epoch number indicating the current phase
          of training
      precision (int): Precision of the number format in decimal places
  
  State Requirements:
      - :attr:`torchbearer.state.EPOCH`: The current epoch number
      - :attr:`torchbearer.state.MAX_EPOCHS`: The total number of epochs for this run
      - :attr:`torchbearer.state.BATCH`: The current batch / iteration number
      - :attr:`torchbearer.state.STEPS`: The total number of steps / batches / iterations for this epoch
      - :attr:`torchbearer.state.METRICS`: The metrics dict to print
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code: def _step(self, state, letter, steps):
      epoch_str = "{:d}/{:d}({:s}): ".format(
          state[torchbearer.EPOCH],
          state[torchbearer.MAX_EPOCHS],
          letter,
      )
      batch_str = "{:d}/{:d} ".format(state[torchbearer.BATCH], steps)
      stats_str = _format_metrics(
          state[torchbearer.METRICS], self.rounder
      )
      print("\r" + epoch_str + batch_str + stats_str, end="")
  
imports: from __future__ import print_function
  from collections import OrderedDict
  from functools import partial
  from tqdm import tqdm
  import torchbearer
  from torchbearer.callbacks import Callback
label_desc: Use image node_id 2 for calling the ConsolePrinter obj's underlying member method code with example usage: obj._step(state, letter, steps) without return types