class_docstr: Zero initialisation for the ``bias`` attributes of filtered modules. This is recommended for use in conjunction
  with weight initialisation schemes.
  
  Example: ::
  
      >>> import torch
      >>> import torch.nn as nn
      >>> from torchbearer import Trial
      >>> from torchbearer.callbacks.init import ZeroBias
  
      # 100 random data points
      >>> data = torch.rand(100, 3, 5, 5)
      >>> example_batch = data[:3]
      >>> initialiser = ZeroBias()
  
      # Model and trail using zero bias init for some random data
      >>> model = nn.Sequential(nn.Conv2d(3, 1, 3), nn.ReLU())
      >>> trial = Trial(model, callbacks=[initialiser]).with_train_data(data, data+5)
  
  Args:
      modules (Iterable[nn.Module] or nn.Module, optional): an iterable of nn.Modules or a
          single nn.Module that will have weights initialised, otherwise this is retrieved from the model
      targets (list[String]): A list of lookup strings to match which modules will be initialised
class_docstr_tok: ["Zero","initialisation","for","the","``","bias","``","attributes","of","filtered","modules",".","This","is","recommended","for","use","in","conjunction","with","weight","initialisation","schemes",".","Example",":",":",":",">",">",">","import","torch",">",">",">","import","torch.nn","as","nn",">",">",">","from","torchbearer","import","Trial",">",">",">","from","torchbearer.callbacks.init","import","ZeroBias","#","100","random","data","points",">",">",">","data","=","torch.rand","(","100",",","3",",","5",",","5",")",">",">",">","example_batch","=","data","[",":3","]",">",">",">","initialiser","=","ZeroBias","(",")","#","Model","and","trail","using","zero","bias","init","for","some","random","data",">",">",">","model","=","nn.Sequential","(","nn.Conv2d","(","3",",","1",",","3",")",",","nn.ReLU","(",")",")",">",">",">","trial","=","Trial","(","model",",","callbacks=","[","initialiser","]",")",".with_train_data","(","data",",","data+5",")","Args",":","modules","(","Iterable","[","nn.Module","]","or","nn.Module",",","optional",")",":","an","iterable","of","nn.Modules","or","a","single","nn.Module","that","will","have","weights","initialised",",","otherwise","this","is","retrieved","from","the","model","targets","(","list","[","String","]",")",":","A","list","of","lookup","strings","to","match","which","modules","will","be","initialised"]
code: def __init__(
      self, modules=None, targets=["Conv", "Linear", "Bilinear"]
  ):
      def initialiser(module):
          module.bias.data.zero_()
  
      super(ZeroBias, self).__init__(
          initialiser, modules=modules, targets=targets
      )
  
imports: import torchbearer
  from torchbearer import cite
  from torchbearer.callbacks import Callback
  import torch.nn.init
label_desc: Use image node_id 1 to create a new ZeroBias object from inherited base classes: WeightInit with example: obj = ZeroBias(modules, targets)