parameters: (labels, weights, predictions)
return_statement: return unweighted_loss * weights, control_flow_ops.no_op()
docstring: Squared loss given labels, example weights and predictions.
  
    Args:
      labels: Rank 2 (N, D) tensor of per-example labels.
      weights: Rank 2 (N, 1) tensor of per-example weights.
      predictions: Rank 2 (N, D) tensor of per-example predictions.
  
    Returns:
      loss: A Rank 2 (N, 1) tensor of per-example squared loss.
      update_op: An update operation to update the loss's internal state.
docstring_summary: Squared loss given labels, example weights and predictions.
docstring_tokens: Squared loss given labels example weights and predictions .
function: def per_example_squared_loss(labels, weights, predictions):
    """Squared loss given labels, example weights and predictions.
  
    Args:
      labels: Rank 2 (N, D) tensor of per-example labels.
      weights: Rank 2 (N, 1) tensor of per-example weights.
      predictions: Rank 2 (N, D) tensor of per-example predictions.
  
    Returns:
      loss: A Rank 2 (N, 1) tensor of per-example squared loss.
      update_op: An update operation to update the loss's internal state.
    """
    unweighted_loss = math_ops.reduce_sum(
        math_ops.squared_difference(predictions, labels), 1, keepdims=True)
  
    return unweighted_loss * weights, control_flow_ops.no_op()
function_tokens: def per_example_squared_loss ( labels , weights , predictions ) : unweighted_loss = math_ops . reduce_sum ( math_ops . squared_difference ( predictions , labels ) , 1 , keepdims = True ) return unweighted_loss * weights , control_flow_ops . no_op ( )