Fixes for Turing 0.37 compatibility

Author: penelopeysm

developers/compiler/model-manual/index.qmd

@@ -28,6 +28,8 @@ However, models can be constructed by hand without the use of a macro.
 Taking the `gdemo` model above as an example, the macro-based definition can be implemented also (a bit less generally) with the macro-free version
 
 ```{julia}
+using DynamicPPL
+
 # Create the model function.
 function gdemo2(model, varinfo, context, x)
     # Assume s² has an InverseGamma distribution.

tutorials/infinite-mixture-models/index.qmd

@@ -188,10 +188,10 @@ In Turing we can implement an infinite Gaussian mixture model using the Chinese
     H = Normal(μ0, σ0)
 
     # Latent assignment.
-    z = tzeros(Int, length(x))
+    z = zeros(Int, length(x))
 
     # Locations of the infinitely many clusters.
-    μ = tzeros(Float64, 0)
+    μ = zeros(Float64, 0)
 
     for i in 1:length(x)
 

usage/probability-interface/index.qmd

@@ -18,7 +18,7 @@ Let's use a simple model of normally-distributed data as an example.
 
 ```{julia}
 using Turing
-using LinearAlgebra: I
+using DynamicPPL
 using Random
 
 @model function gdemo(n)
@@ -98,11 +98,9 @@ logjoint(model, sample)
 ```
 
 For models with many variables `rand(model)` can be prohibitively slow since it returns a `NamedTuple` of samples from the prior distribution of the unconditioned variables.
-We recommend working with samples of type `DataStructures.OrderedDict` in this case:
+We recommend working with samples of type `DataStructures.OrderedDict` in this case (which Turing re-exports, so can be used directly):
 
 ```{julia}
-using DataStructures: OrderedDict
-
 Random.seed!(124)
 sample_dict = rand(OrderedDict, model)
 ```