Randomly zero out entire channels (a channel is a 2D feature map,
e.g., the \(j\)-th channel of the \(i\)-th sample in the
batched input is a 2D tensor \(input[i, j]\)) of the input tensor).
Each channel will be zeroed out independently on every forward call with
probability `p`

using samples from a Bernoulli distribution.

## Usage

`nnf_dropout2d(input, p = 0.5, training = TRUE, inplace = FALSE)`

## Arguments

- input
the input tensor

- p
probability of a channel to be zeroed. Default: 0.5

- training
apply dropout if is `TRUE`

. Default: `TRUE`

.

- inplace
If set to `TRUE`

, will do this operation in-place.
Default: `FALSE`