/// <summary>
/// Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D inputs with optional additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .
/// </summary>
/// <param name="x"></param>
/// <param name="features">C from an expected input of size (N,C,L) or LL from input of size (N, L)</param>
/// <param name="eps">A value added to the denominator for numerical stability. Default: 1e-5</param>
/// <param name="momentum">The value used for the running_mean and running_var computation. Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1</param>
/// <param name="affine">A boolean value that when set to True, this module has learnable affine parameters. Default: true</param>
/// <param name="track_running_stats">A boolean value that when set to True, this module tracks the running mean and variance, and when set to False,
/// this module does not track such statistics, and initializes statistics buffers running_mean and running_var as None.
/// When these buffers are None, this module always uses batch statistics. in both training and eval modes. Default: true</param>
/// <returns></returns>
static public TorchTensor BatchNorm1d(TorchTensor x, long features, double eps = 1e-05, double momentum = 0.1, bool affine = true, bool track_running_stats = true)
{
using (var d = Modules.BatchNorm1d(features, eps, momentum, affine, track_running_stats)) {
return(d.forward(x));
}
}
