Include docstring of DeepONet and NOMAD

Author: foldfelis

docs/src/apis.md

@@ -1,5 +1,11 @@
 # APIs
 
+## Transforms
+
+```@docs
+AbstractTransform
+```
+
 ## Layers
 
 ### Operator convolutional layer
@@ -75,3 +81,20 @@ MarkovNeuralOperator
 ```
 
 Reference: [MNO2021](@cite)
+
+---
+
+### DeepONet
+
+```@docs
+DeepONet
+NeuralOperators.construct_subnet
+```
+
+### NOMAD
+
+Nonlinear manifold decoders for operator learning
+
+```@docs
+NOMAD
+```

src/NOMAD.jl

@@ -4,12 +4,12 @@ struct NOMAD{T1, T2}
 end
 
 """
-`NOMAD(architecture_approximator::Tuple, architecture_decoder::Tuple,
-        act_approximator = identity, act_decoder=true;
-        init_approximator = Flux.glorot_uniform,
-        init_decoder = Flux.glorot_uniform,
-        bias_approximator=true, bias_decoder=true)`
-`NOMAD(approximator_net::Flux.Chain, decoder_net::Flux.Chain)`
+    NOMAD(architecture_approximator::Tuple, architecture_decoder::Tuple,
+          act_approximator = identity, act_decoder=true;
+          init_approximator = Flux.glorot_uniform,
+          init_decoder = Flux.glorot_uniform,
+          bias_approximator=true, bias_decoder=true)
+    NOMAD(approximator_net::Flux.Chain, decoder_net::Flux.Chain)
 
 Create a Nonlinear Manifold Decoders for Operator Learning (NOMAD) as proposed by Lu et al.
 arXiv:2206.03551
@@ -47,6 +47,7 @@ julia> model = NOMAD(approximator, decoder)
 NOMAD with
 Approximator net: (Chain(Dense(2 => 128), Dense(128 => 64)))
 Decoder net: (Chain(Dense(72 => 24), Dense(24 => 12)))
+```
 """
 function NOMAD(architecture_approximator::Tuple, architecture_decoder::Tuple,
                act_approximator = identity, act_decoder = true;

src/Transform/fourier_transform.jl

@@ -14,7 +14,7 @@ function low_pass(ft::FourierTransform, 𝐱_fft::AbstractArray)
     return view(𝐱_fft, map(d -> 1:d, ft.modes)..., :, :) # [ft.modes..., in_chs, batch]
 end
 
-const truncate_modes = low_pass
+truncate_modes(args...) = low_pass(args...)
 
 function inverse(ft::FourierTransform, 𝐱_fft::AbstractArray)
     return real(ifft(𝐱_fft, 1:ndims(ft))) # [size(x_fft)..., out_chs, batch]