function conv2dTranspose$1(args) {
	  return new Conv2DTranspose(args);
	}
Transposed convolutional layer (sometimes called Deconvolution).

The need for transposed convolutions generally arises
from the desire to use a transformation going in the opposite direction of
a normal convolution, i.e., from something that has the shape of the output
of some convolution to something that has the shape of its input while
maintaining a connectivity pattern that is compatible with said
convolution.

When using this layer as the first layer in a model, provide the
configuration `inputShape` (`Array` of integers, does not include the
sample axis), e.g., `inputShape: [128, 128, 3]` for 128x128 RGB pictures in
`dataFormat: 'channelsLast'`.

Input shape:
  4D tensor with shape:
  `[batch, channels, rows, cols]` if `dataFormat` is `'channelsFirst'`.
  or 4D tensor with shape
  `[batch, rows, cols, channels]` if `dataFormat` is `'channelsLast`.

Output shape:
  4D tensor with shape:
  `[batch, filters, newRows, newCols]` if `dataFormat` is
`'channelsFirst'`. or 4D tensor with shape:
  `[batch, newRows, newCols, filters]` if `dataFormat` is `'channelsLast'`.

References:
  - [A guide to convolution arithmetic for deep
learning](https://arxiv.org/abs/1603.07285v1)
  - [Deconvolutional
Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)

@doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}