Simply pushes the input forward.
Can be useful during `Graph`

construction using the `%>>%`

-operator to specify which `PipeOp`

gets connected to which.

## Construction

`id`

::`character(1)`

Identifier of resulting object, default`"nop"`

.`param_vals`

:: named`list`

List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default`list()`

.

## Input and Output Channels

`PipeOpNOP`

has one input channel named `"input"`

, taking any input (`"*"`

) both during training and prediction.

`PipeOpNOP`

has one output channel named `"output"`

, producing the object given as input (`"*"`

) without changes.

## State

The `$state`

is left empty (`list()`

).

## Fields

Only fields inherited from `PipeOp`

.

## Methods

Only methods inherited from `PipeOp`

.

## See also

https://mlr-org.com/pipeops.html

Other PipeOps:
`PipeOp`

,
`PipeOpEnsemble`

,
`PipeOpImpute`

,
`PipeOpTargetTrafo`

,
`PipeOpTaskPreproc`

,
`PipeOpTaskPreprocSimple`

,
`mlr_pipeops`

,
`mlr_pipeops_adas`

,
`mlr_pipeops_blsmote`

,
`mlr_pipeops_boxcox`

,
`mlr_pipeops_branch`

,
`mlr_pipeops_chunk`

,
`mlr_pipeops_classbalancing`

,
`mlr_pipeops_classifavg`

,
`mlr_pipeops_classweights`

,
`mlr_pipeops_colapply`

,
`mlr_pipeops_collapsefactors`

,
`mlr_pipeops_colroles`

,
`mlr_pipeops_copy`

,
`mlr_pipeops_datefeatures`

,
`mlr_pipeops_encode`

,
`mlr_pipeops_encodeimpact`

,
`mlr_pipeops_encodelmer`

,
`mlr_pipeops_featureunion`

,
`mlr_pipeops_filter`

,
`mlr_pipeops_fixfactors`

,
`mlr_pipeops_histbin`

,
`mlr_pipeops_ica`

,
`mlr_pipeops_imputeconstant`

,
`mlr_pipeops_imputehist`

,
`mlr_pipeops_imputelearner`

,
`mlr_pipeops_imputemean`

,
`mlr_pipeops_imputemedian`

,
`mlr_pipeops_imputemode`

,
`mlr_pipeops_imputeoor`

,
`mlr_pipeops_imputesample`

,
`mlr_pipeops_kernelpca`

,
`mlr_pipeops_learner`

,
`mlr_pipeops_missind`

,
`mlr_pipeops_modelmatrix`

,
`mlr_pipeops_multiplicityexply`

,
`mlr_pipeops_multiplicityimply`

,
`mlr_pipeops_mutate`

,
`mlr_pipeops_nmf`

,
`mlr_pipeops_ovrsplit`

,
`mlr_pipeops_ovrunite`

,
`mlr_pipeops_pca`

,
`mlr_pipeops_proxy`

,
`mlr_pipeops_quantilebin`

,
`mlr_pipeops_randomprojection`

,
`mlr_pipeops_randomresponse`

,
`mlr_pipeops_regravg`

,
`mlr_pipeops_removeconstants`

,
`mlr_pipeops_renamecolumns`

,
`mlr_pipeops_replicate`

,
`mlr_pipeops_rowapply`

,
`mlr_pipeops_scale`

,
`mlr_pipeops_scalemaxabs`

,
`mlr_pipeops_scalerange`

,
`mlr_pipeops_select`

,
`mlr_pipeops_smote`

,
`mlr_pipeops_smotenc`

,
`mlr_pipeops_spatialsign`

,
`mlr_pipeops_subsample`

,
`mlr_pipeops_targetinvert`

,
`mlr_pipeops_targetmutate`

,
`mlr_pipeops_targettrafoscalerange`

,
`mlr_pipeops_textvectorizer`

,
`mlr_pipeops_threshold`

,
`mlr_pipeops_tunethreshold`

,
`mlr_pipeops_unbranch`

,
`mlr_pipeops_updatetarget`

,
`mlr_pipeops_vtreat`

,
`mlr_pipeops_yeojohnson`

Other Placeholder Pipeops:
`mlr_pipeops_copy`

## Examples

```
library("mlr3")
nop = po("nop")
nop$train(list(1))
#> $output
#> [1] 1
#>
# use `gunion` and `%>>%` to create a "bypass"
# next to "pca"
gr = gunion(list(
po("pca"),
nop
)) %>>% po("featureunion")
gr$train(tsk("iris"))[[1]]$data()
#> Species PC1 PC2 PC3 PC4 Petal.Length
#> <fctr> <num> <num> <num> <num> <num>
#> 1: setosa -2.684126 -0.31939725 0.02791483 -0.002262437 1.4
#> 2: setosa -2.714142 0.17700123 0.21046427 -0.099026550 1.4
#> 3: setosa -2.888991 0.14494943 -0.01790026 -0.019968390 1.3
#> 4: setosa -2.745343 0.31829898 -0.03155937 0.075575817 1.5
#> 5: setosa -2.728717 -0.32675451 -0.09007924 0.061258593 1.4
#> ---
#> 146: virginica 1.944110 -0.18753230 -0.17782509 -0.426195940 5.2
#> 147: virginica 1.527167 0.37531698 0.12189817 -0.254367442 5.0
#> 148: virginica 1.764346 -0.07885885 -0.13048163 -0.137001274 5.2
#> 149: virginica 1.900942 -0.11662796 -0.72325156 -0.044595305 5.4
#> 150: virginica 1.390189 0.28266094 -0.36290965 0.155038628 5.1
#> Petal.Width Sepal.Length Sepal.Width
#> <num> <num> <num>
#> 1: 0.2 5.1 3.5
#> 2: 0.2 4.9 3.0
#> 3: 0.2 4.7 3.2
#> 4: 0.2 4.6 3.1
#> 5: 0.2 5.0 3.6
#> ---
#> 146: 2.3 6.7 3.0
#> 147: 1.9 6.3 2.5
#> 148: 2.0 6.5 3.0
#> 149: 2.3 6.2 3.4
#> 150: 1.8 5.9 3.0
```