parameters: (parser, train_data, dev_data, optimizer, loss_func, batch_size)
return_statement: return dev_UAS
docstring: Train the neural dependency parser for single epoch.
  
      Note: In PyTorch we can signify train versus test and automatically have
      the Dropout Layer applied and removed, accordingly, by specifying
      whether we are training, `model.train()`, or evaluating, `model.eval()`
  
      @param parser (Parser): Neural Dependency Parser
      @param train_data ():
      @param dev_data ():
      @param optimizer (nn.Optimizer): Adam Optimizer
      @param loss_func (nn.CrossEntropyLoss): Cross Entropy Loss Function
      @param batch_size (int): batch size
      @param lr (float): learning rate
  
      @return dev_UAS (float): Unlabeled Attachment Score (UAS) for dev data
docstring_summary: Train the neural dependency parser for single epoch.
docstring_tokens: Train the neural dependency parser for single epoch .
function: def train_for_epoch(parser, train_data, dev_data, optimizer, loss_func, batch_size):
      """ Train the neural dependency parser for single epoch.
  
      Note: In PyTorch we can signify train versus test and automatically have
      the Dropout Layer applied and removed, accordingly, by specifying
      whether we are training, `model.train()`, or evaluating, `model.eval()`
  
      @param parser (Parser): Neural Dependency Parser
      @param train_data ():
      @param dev_data ():
      @param optimizer (nn.Optimizer): Adam Optimizer
      @param loss_func (nn.CrossEntropyLoss): Cross Entropy Loss Function
      @param batch_size (int): batch size
      @param lr (float): learning rate
  
      @return dev_UAS (float): Unlabeled Attachment Score (UAS) for dev data
      """
      parser.model.train() # Places model in "train" mode, i.e. apply dropout layer
      n_minibatches = math.ceil(len(train_data) / batch_size)
      loss_meter = AverageMeter()
  
      with tqdm(total=(n_minibatches)) as prog:
          for i, (train_x, train_y) in enumerate(minibatches(train_data, batch_size)):
              optimizer.zero_grad()   # remove any baggage in the optimizer
              loss = 0. # store loss for this batch here
              train_x = torch.from_numpy(train_x).long()
              train_y = torch.from_numpy(train_y.nonzero()[1]).long()
  
              ### YOUR CODE HERE (~5-10 lines)
              ### TODO:
              ###      1) Run train_x forward through model to produce `logits`
              ###      2) Use the `loss_func` parameter to apply the PyTorch CrossEntropyLoss function.
              ###         This will take `logits` and `train_y` as inputs. It will output the CrossEntropyLoss
              ###         between softmax(`logits`) and `train_y`. Remember that softmax(`logits`)
              ###         are the predictions (y^ from the PDF).
              ###      3) Backprop losses
              ###      4) Take step with the optimizer
              ### Please see the following docs for support:
              ###     Optimizer Step: https://pytorch.org/docs/stable/optim.html#optimizer-step
              logits = model(train_x)
  
              optimizer.zero_grad()
              loss = loss_func(logits, train_y)
              loss.backward()
              optimizer.step()
  
              ### END YOUR CODE
              prog.update(1)
              loss_meter.update(loss.item())
  
      print ("Average Train Loss: {}".format(loss_meter.avg))
  
      print("Evaluating on dev set",)
      parser.model.eval() # Places model in "eval" mode, i.e. don't apply dropout layer
      dev_UAS, _ = parser.parse(dev_data)
      print("- dev UAS: {:.2f}".format(dev_UAS * 100.0))
      return dev_UAS
function_tokens: def train_for_epoch ( parser , train_data , dev_data , optimizer , loss_func , batch_size ) : parser . model . train ( ) # Places model in "train" mode, i.e. apply dropout layer n_minibatches = math . ceil ( len ( train_data ) / batch_size ) loss_meter = AverageMeter ( ) with tqdm ( total = ( n_minibatches ) ) as prog : for i , ( train_x , train_y ) in enumerate ( minibatches ( train_data , batch_size ) ) : optimizer . zero_grad ( ) # remove any baggage in the optimizer loss = 0. # store loss for this batch here train_x = torch . from_numpy ( train_x ) . long ( ) train_y = torch . from_numpy ( train_y . nonzero ( ) [ 1 ] ) . long ( ) ### YOUR CODE HERE (~5-10 lines) ### TODO: ###      1) Run train_x forward through model to produce `logits` ###      2) Use the `loss_func` parameter to apply the PyTorch CrossEntropyLoss function. ###         This will take `logits` and `train_y` as inputs. It will output the CrossEntropyLoss ###         between softmax(`logits`) and `train_y`. Remember that softmax(`logits`) ###         are the predictions (y^ from the PDF). ###      3) Backprop losses ###      4) Take step with the optimizer ### Please see the following docs for support: ###     Optimizer Step: https://pytorch.org/docs/stable/optim.html#optimizer-step logits = model ( train_x ) optimizer . zero_grad ( ) loss = loss_func ( logits , train_y ) loss . backward ( ) optimizer . step ( ) ### END YOUR CODE prog . update ( 1 ) loss_meter . update ( loss . item ( ) ) print ( "Average Train Loss: {}" . format ( loss_meter . avg ) ) print ( "Evaluating on dev set" , ) parser . model . eval ( ) # Places model in "eval" mode, i.e. don't apply dropout layer dev_UAS , _ = parser . parse ( dev_data ) print ( "- dev UAS: {:.2f}" . format ( dev_UAS * 100.0 ) ) return dev_UAS