class_docstr: Wrapper to track the statistics of the weights' gradient per layer and log them in TensorBoard per epoch.
  
  Args:
      writer (~torch.utils.tensorboard.writer.SummaryWriter): The TensorBoard writer.
      keep_bias (bool): Either or not to log the bias of the network.
  
  Example:
      Using TensorBoardGradientTracker::
  
          from torch.utils.tensorboard import SummaryWriter
          from poutyne import Model, TensorBoardGradientTracker
  
          writer = SummaryWriter('runs')
          tb_tracker = TensorBoardGradientTracker(writer)
  
          model = Model(...)
          model.fit_generator(..., callbacks=[tb_tracker])
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code: def __init__(self, writer, keep_bias: bool = False) -> None:
      super().__init__(keep_bias)
  
      self.writer = writer
  
imports: from typing import Dict, Iterable, List, Tuple
  import numpy
  import torch
  from poutyne.framework.callbacks.callbacks import Callback
label_desc: Use image node_id 1 to create a new TensorBoardGradientTracker object from inherited base classes: GradientTracker with example: obj = TensorBoardGradientTracker(writer, keep_bias)