BoundsError when inverting a model with Turing

New to Julia
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2

Ok, the following converts the data to a Vector{Matrix{Float64}} of size (12000,):

d = [hcat(c[:]) for c in eachrow(data)]
d[:,1] ~ Normal(predicted[:,1]) 
d[:,2] ~ Normal(predicted[:,2])

but this was not the solution after all - I still get the same BoundsError in the same place.

Any suggestions of what else may be wrong with my code/approach would be very welcome.