If the input layer $\text{'}$X$\text{'}$ denote the one hot encoding of the vocabulary, $"$e$"$ is the embedding layer, $"$h$1","$h$2"$ are hidden layers with sigmoid activation function and $"$Y$"$ is the output layer emitting continuous valued output, identify no more than $2$ issues$/$error in the architecture for each of the below two scenarios: If there are no corrections required then mention $"$No Error" explicitly. Assuming no bias is included in the network design, find the dimension MOYmatrix size$)$ w$.$r$.$t to section$/$layer $"$W$1"$ and $"$U$"$

Use Case: Given a training corpus with below vocabulary each vectorized with three dimensions, and following test sentence phrase, the neural network, should have predictive ability to classify given phrase of two tokens into positive, negative or neutral sentiment$.$ Vocabulary: $($today$,$ tomorrow, sunny, rainy, day, like, season, dislike, icecream, chocolate, is$,$ was,

both. I

Test Sentence: "I dislike icecream"