Pytorch, how to get the parameters of my network
I have a question about getting all parameters of the network. My network is defined as follow:
activation = nn.ReLU()
class OneInputBasis(nn.Module):
    def __init__(self):
        super().__init__()
        
        bo_b = True
        bo_last = False

        self.l1 = nn.Linear(200, 100, bias = bo_b).to(device)
        self.l4 = nn.Linear(100, 100, bias = bo_last).to(device)
        
    def forward(self, v):
        v = activation ( self.l1(v) )
        v = ( self.l4(v) )        

        return v

and
class node(nn.Module):
    def __init__(self):
        super().__init__()
        
        bo_b = True
        bo_last = False
        
        self.set_lay = []
        
        for jj in range(dim_output_space_basis):
            self.set_lay.append(OneInputBasis())
        
        
    def forward(self, v):

        w = self.set_lay[0](v)

        for ii in range(dim_output_space_basis-1):
            w = torch.cat((w, self.set_lay[ii+1](v)), dim = 1 )
        
        return w

and
class mesh(nn.Module):
    def __init__(self):
        super().__init__()
        
        bo_b = True
        bo_last = False

        self.l3 = nn.Linear(2, 100, bias = bo_b).to(device)
        self.l4 = nn.Linear(100, 100, bias = bo_b).to(device)
        self.l7 = nn.Linear(100,10, bias = bo_last).to(device)
        
    def forward(self, w):
        w = activation ( self.l3(w) )
        w = activation ( self.l4(w) )
        w =  ( self.l7(w) )
        
        return w

finally, I have
activation = nn.ReLU()
class Test(nn.Module):
    def __init__(self):
        super().__init__()
        
        bo_b = True
        bo_last = False

        self.top = node()
        self.bottom = mesh()

    def forward(self, v, w, y):
        v = self.top(v)
        w = self.bottom(w)
        e = torch.bmm(w ,torch.bmm(v, y))

        return e[:, :, 0]

Now I define the network:
fnn_adam = Test()

When I print the parameters of the network, as
 for p in fnn_adam.parameters():
     print(p)

I can only see the parameters associated with fnn_adam.bottom, how can I print out the parameters associated with fnn_adam.top? Are the parameters associated with .top trainable? Thank you!

Calling self.set_lay.append(OneInputBasis()) with the instantiation of node does not register the fully-connected layers
 self.l1 = nn.Linear(200, 100, bias = bo_b).to(device)
 self.l4 = nn.Linear(100, 100, bias = bo_last).to(device) 

to the instance fnn_adam of class Test. This is why the respective parameters do not show up in your code above.
Without loss of generality, I chose
import torch
import torch.nn as nn
import torch.nn.functional as F

dim_output_space_basis = 2
device ='cpu'

and modified the init method of class node. The remainder of your code is perfectly fine. Please see below:
class node(nn.Module):
    def __init__(self):
        super().__init__()
        
        bo_b = True
        bo_last = False
        
        # self.set_lay = [] # Legacy
        attributeNames = ['l_btm{}'.format(i) for i in range(dim_output_space_basis)]
        
        for jj_index, jj in enumerate(range(dim_output_space_basis)):
            # self.set_lay.append(OneInputBasis()) # Legacy
            setattr(self, attributeNames[jj_index], OneInputBasis())
    

Now, the parameters register as evidenced by running fnn_adam._modules and observing its output
OrderedDict([('top',
              node(
                (l_btm0): OneInputBasis(
                  (l1): Linear(in_features=200, out_features=100, bias=True)
                  (l4): Linear(in_features=100, out_features=100, bias=False)
                )
                (l_btm1): OneInputBasis(
                  (l1): Linear(in_features=200, out_features=100, bias=True)
                  (l4): Linear(in_features=100, out_features=100, bias=False)
                )
              )),
             ('bottom',
              mesh(
                (l3): Linear(in_features=2, out_features=100, bias=True)
                (l4): Linear(in_features=100, out_features=100, bias=True)
                (l7): Linear(in_features=100, out_features=10, bias=False)
              ))])

