function minimum$1(config) {
	  if (Array.isArray(config)) {
	    var layer = new Minimum$1({});
	    return layer.apply(config);
	  } else {
	    return new Minimum$1(config);
	  }
	}
Calculate the element-wise minimum of inputs, which all have the same shape.

This function can be invoked in three ways.

1. Construct an instance of `Minimum` layer, by using no input argument
   or a single configuration argument. The resultant `Minimum` layer can then
   be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:

```js
const minimumLayer = tf.layers.minimum();

// The layer can be applied to inputs.
const input1 = tf.input({shape: [2, 2]});
const input2 = tf.input({shape: [2, 2]});
const output = minimumLayer.apply([input1, input2]);
console.log(output.shape);
// You get [null, 2, 2], with the first dimension as the undetermined batch
// dimension.
```

2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs
   an `Layer` object internally and calls its `apply` method on the inputs,
   generating a new `tf.SymbolicTensor`. For example:

```js
const input1 = tf.input({shape: [2, 2]});
const input2 = tf.input({shape: [2, 2]});
const output = tf.layers.minimum([input1, input2]);
console.log(output.shape);
// You get [null, 2, 2], with the first dimension as the undetermined batch
// dimension.
```

3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs
   an `Layer` object internally and calls its `apply` method on the inputs,
   generating a new `tf.Tensor` as the result of the computation. For
example:

```js
const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);
const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);
tf.layers.minimum([input1, input2]).print();
// Gives [[1, 2], [3, 4]].