function meanAbsoluteError$1(yTrue, yPred) {
	  return meanAbsoluteError(yTrue, yPred);
	}
Loss or metric function: Mean absolute error.

Mathematically, mean absolute error is defined as:
  `mean(abs(yPred - yTrue))`,
wherein the `mean` is applied over feature dimensions.

```js
const yTrue = tf.tensor2d([[0, 1], [0, 0], [2, 3]]);
const yPred = tf.tensor2d([[0, 1], [0, 1], [-2, -3]]);
const mse = tf.metrics.meanAbsoluteError(yTrue, yPred);
mse.print();
```

@param yTrue Truth Tensor.
@param yPred Prediction Tensor.
@return Mean absolute error Tensor.

@doc {heading: 'Metrics', namespace: 'metrics'}