parameters: (tensor_in, labels, weights, biases, name=None)
docstring: Returns prediction and loss for mean squared error regression.
docstring_summary: Returns prediction and loss for mean squared error regression.
docstring_tokens: Returns prediction and loss for mean squared error regression .
function: def mean_squared_error_regressor(tensor_in, labels, weights, biases, name=None):
    """Returns prediction and loss for mean squared error regression."""
    with ops.name_scope(name, 'mean_squared_error_regressor',
                        [tensor_in, labels]):
      predictions = nn.xw_plus_b(tensor_in, weights, biases)
      if len(labels.get_shape()) == 1 and len(predictions.get_shape()) == 2:
        predictions = array_ops_.squeeze(predictions, squeeze_dims=[1])
      return predictions, loss_ops.mean_squared_error(predictions, labels)
function_tokens: def mean_squared_error_regressor ( tensor_in , labels , weights , biases , name = None ) : with ops . name_scope ( name , 'mean_squared_error_regressor' , [ tensor_in , labels ] ) : predictions = nn . xw_plus_b ( tensor_in , weights , biases ) if len ( labels . get_shape ( ) ) == 1 and len ( predictions . get_shape ( ) ) == 2 : predictions = array_ops_ . squeeze ( predictions , squeeze_dims = [ 1 ] ) return predictions , loss_ops . mean_squared_error ( predictions , labels )