original_docstring: 
      Compute the accuracy, given the outputs and labels for all images.
  
      Args:
          outputs: (np.ndarray) dimension batch_size x 6 - log softmax output of the model
          labels: (np.ndarray) dimension batch_size, where each element is a value in [0, 1, 2, 3, 4, 5]
  
      Returns: (float) accuracy in [0,1]
      
docstring: Compute the accuracy, given the outputs and labels for all images.
docstring_tokens: Compute the accuracy given the outputs and labels for all images .
code: def accuracy(outputs, labels):
      """
      Compute the accuracy, given the outputs and labels for all images.
  
      Args:
          outputs: (np.ndarray) dimension batch_size x 6 - log softmax output of the model
          labels: (np.ndarray) dimension batch_size, where each element is a value in [0, 1, 2, 3, 4, 5]
  
      Returns: (float) accuracy in [0,1]
      """
      outputs = np.argmax(outputs, axis=1)
      return np.sum(outputs==labels)/float(labels.size)
code_tokens: def accuracy ( outputs , labels ) : outputs = np . argmax ( outputs , axis = 1 ) return np . sum ( outputs == labels ) / float ( labels . size )
short_docstring: Compute the accuracy, given the outputs and labels for all images.
short_docstring_tokens: Compute the accuracy given the outputs and labels for all images .
comment: """
      Compute the accuracy, given the outputs and labels for all images.
  
      Args:
          outputs: (np.ndarray) dimension batch_size x 6 - log softmax output of the model
          labels: (np.ndarray) dimension batch_size, where each element is a value in [0, 1, 2, 3, 4, 5]
  
      Returns: (float) accuracy in [0,1]
      """