This question and the next one use the following context. Consider a modified version of the one$-$layer Deep Averaging Network $($DAN$)$ with the following architecture:

Input: Sequence of word embeddings $21,22,...,$ In$,$ each of dimension $50.$

PyTorch layers: $(1)$ Linear layer $($input$=50,$ output$=50)$: $(2)$ ReLU; $(3)$ Averaging layer; $(4)$ Linear layer $($input$=50,$ output$=50)$; $(5)$ ReLU; $(6)$ Linear layer $($input$=50,$ output$=2)$; $(7)$ Softmax

Given the network as is$,$ what will be the biggest reason that it may fail to learn a task compared to a basic DAN?

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A$.$ The softmax cannot "peak" enough on the right answer $($the logits are too small$)$

$$B$.$ It doesn't correctly implement a nonlinear computation

$$C$.$ There are too many linear layers, leading to too many parameters

$$D$.$ None of the above