original_docstring: 
      evaluation for both the dev and test set
      calculates the accuracy for the corresponding dataset
      
docstring: evaluation for both the dev and test set
  calculates the accuracy for the corresponding dataset
docstring_tokens: evaluation for both the dev and test set calculates the accuracy for the corresponding dataset
code: def evaluate_pos(test_x, test_y, model_DC, total_batches_test, batch_size):
      '''
      evaluation for both the dev and test set
      calculates the accuracy for the corresponding dataset
      '''
      correct = 0
      for i in range(total_batches_test):
          if i < total_batches_test-1:
              features = test_x[i*batch_size:(i+1)*batch_size].to(dtype=torch.float, device=device)
              labels = test_y[i*batch_size:(i+1)*batch_size].to(dtype=torch.long, device=device)
          else:
              features = test_x[i*batch_size:].to(dtype=torch.float, device=device)
              labels = test_y[i*batch_size:].to(dtype=torch.long, device=device)
          
          preds = model_DC(features)
          correct += torch.sum((preds.max(1)[1] == labels).int()).item()
  
      acc_test = correct/len(test_x)
      print("test set accuracy: " + str(acc_test))
      print("---------------------------------------------------------")
code_tokens: def evaluate_pos ( test_x , test_y , model_DC , total_batches_test , batch_size ) : correct = 0 for i in range ( total_batches_test ) : if i < total_batches_test - 1 : features = test_x [ i * batch_size : ( i + 1 ) * batch_size ] . to ( dtype = torch . float , device = device ) labels = test_y [ i * batch_size : ( i + 1 ) * batch_size ] . to ( dtype = torch . long , device = device ) else : features = test_x [ i * batch_size : ] . to ( dtype = torch . float , device = device ) labels = test_y [ i * batch_size : ] . to ( dtype = torch . long , device = device ) preds = model_DC ( features ) correct += torch . sum ( ( preds . max ( 1 ) [ 1 ] == labels ) . int ( ) ) . item ( ) acc_test = correct / len ( test_x ) print ( "test set accuracy: " + str ( acc_test ) ) print ( "---------------------------------------------------------" )
short_docstring: evaluation for both the dev and test set
  calculates the accuracy for the corresponding dataset
short_docstring_tokens: evaluation for both the dev and test set calculates the accuracy for the corresponding dataset
comment: '''
      evaluation for both the dev and test set
      calculates the accuracy for the corresponding dataset
      '''