Thank you for your time!
Here it is:
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-363-0058cef7d67b> in <module>
      9 
     10 import numpy as np
---> 11 train(model,train_dataloader,validation_dataloader)
     12 print("")
     13 print("Training complete!")

<ipython-input-348-f7eefdedff7c> in train(model, train_dataloader, validation_dataloader)
     72             # The documentation for this `model` function is here:
     73             # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
---> 74             outputs = model(batch)
     75 
     76             # The call to `model` always returns a tuple, so we need to pull the

/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

<ipython-input-362-052e4983c882> in forward(self, batch)
     49         pooled_output = pooled_output.unsqueeze(0)
     50 
---> 51         pooled_output_1 = self.dropout_1(self.bn(pooled_output))
     52         pooled_output_2 = self.dropout_2(self.bn2(pooled_output))
     53 

/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/batchnorm.py in forward(self, input)
    105             input, self.running_mean, self.running_var, self.weight, self.bias,
    106             self.training or not self.track_running_stats,
--> 107             exponential_average_factor, self.eps)
    108 
    109 

/opt/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py in batch_norm(input, running_mean, running_var, weight, bias, training, momentum, eps)
   1664             size_prods *= size[i + 2]
   1665         if size_prods == 1:
-> 1666             raise ValueError('Expected more than 1 value per channel when training, got input size {}'.format(size))
   1667 
   1668     return torch.batch_norm(

ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 16])