/// <summary>
/// Randomly zero out entire channels (a channel is a 3D feature map, e.g., the jj -th channel of the ii -th sample in the batched input is a 3D tensor \text{input}[i, j]input[i,j] ).
/// Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.
/// </summary>
/// <param name="x">Input tensor</param>
/// <param name="probability">Probability of an element to be zeroed. Default: 0.5</param>
/// <param name="inPlace">If set to true, will do this operation in-place. Default: false</param>
/// <returns></returns>
static public TorchTensor Dropout3d(TorchTensor x, double probability = 0.5, bool inPlace = false)
{
using (var d = Modules.Dropout3d(probability, inPlace)) {
return(d.forward(x));
}
}
