getobs
	
function defined in module 
	MLUtils
			getobs(data, [idx])
			Return the observations corresponding to the observation index 
			idx. Note that 
			idx can be any type as long as 
			data has defined 
			getobs for that type. If 
			idx is not provided, then materialize all observations in 
			data.
			If 
			data does not have 
			getobs defined, then in the case of 
			Tables.table(data) == true returns the row(s) in position 
			idx, otherwise returns 
			data[idx].
			Authors of custom data containers should implement 
			Base.getindex for their type instead of 
			getobs. 
			getobs should only be implemented for types where there is a difference between 
			getobs and 
			Base.getindex (such as multi-dimensional arrays).
			The returned observation(s) should be in the form intended to be passed as-is to some learning algorithm. There is no strict interface requirement on how this "actual data" must look like. Every author behind some custom data container can make this decision themselves. The output should be consistent when 
			idx is a scalar vs vector.
			
			getobs supports by default nested combinations of array, tuple, named tuples, and dictionaries.
			# named tuples 
x = (a = [1, 2, 3], b = rand(6, 3))
getobs(x, 2) == (a = 2, b = x.b[:, 2])
getobs(x, [1, 3]) == (a = [1, 3], b = x.b[:, [1, 3]])
# dictionaries
x = Dict(:a => [1, 2, 3], :b => rand(6, 3))
getobs(x, 2) == Dict(:a => 2, :b => x[:b][:, 2])
getobs(x, [1, 3]) == Dict(:a => [1, 3], :b => x[:b][:, [1, 3]])
There are
			16
			methods for MLUtils.getobs:
		
The following pages link back here:
Blocks and encodings, Custom learning tasks, Data containers, Discovery, Feature registries in FastAI.jl, How to visualize data, Image segmentation, Introduction, Keypoint regression, New visualization tools for FastAI.jl, Performant data pipelines, Presizing vision datasets for performance, Saving and loading models for inference, Siamese image similarity, Tabular Classification, Text Classification, TimeSeries Classification, Variational autoencoders, tsregression
FastAI.jl , datasets/Datasets.jl , datasets/recipe.jl , tasks/taskdata.jl , training/utils.jl , FastTabular.jl , recipes.jl , FastVision.jl , blocks/image.jl , encodings/imagepreprocessing.jl , MLUtils.jl , batchview.jl , eachobs.jl , observation.jl , obstransform.jl , obsview.jl , parallel.jl , randobs.jl , utils.jl