I am trying to select an index for my context point selected at random but i keep getting invalid syntax error whenever i run the randomize function

The error only happens when i include the code for randomiser in the network, I have included all my code below

This is the code I used to generate the data

Nf = 2000 # the number of different functions f that we will generate
Npts = 40 # the number of x values that we will use to generate each fa
x = torch.zeros(Nf, Npts, 1)
for k in range(Nf):
x[k,:,0] = torch.linspace(-2, 2, Npts)

x += torch.rand_like(x)*0.1
a = -2 + 4*torch.rand(Nf).view(-1,1).repeat(1, Npts).unsqueeze(2)
y = a*torch.sin(x+a)

The code for the data loader

    dataset = data.TensorDataset(x,y) #TensorDataset object
    #print(dataset[0]) #First example in our dataset
    batch_size = 200
    data_iter = data.DataLoader(dataset, batch_size, shuffle=True) #DataLoader obj

    x,y = next(iter(data_iter))
    print(x.size())
    print (y.shape)

The code defining the randomizer

    def randomiser(n, data):
  if n > 0:
    rand_k = np.zeros(n)
  elif n <= 0:
    raise valueError("n shouldnt be zero or negative")
  
  repeat_check=[]
  ele = 0
  while ele < n:
    r = np.random.randint(len(data))
    if r not in repeat_check:
      rand_k[ele] = data[r]
      repeat_check.append(r)
      ele +=1
    else:
      ele = ele
  return np.int32(rand_k)

The code defining the indexing function

i=0
Nc = randomiser(1, range(3, 37))[0]
def context_indexer (Nc, x, y):
  indx = randomiser(Nc, range(39))
  idx = torch.as_tensor(indx, dtype=torch.int64)

  x_c = torch.index_select(x,1,idx)
  y_c = torch.index_select(y,1,idx)
  return x_c, y_c
x_c, y_c = context_indexer(Nc,x,y)
print(y_c)

The code for the encoder

class Enc(torch.nn.Module):
    def __init__(self, input_dim, output_dim):

        super(Enc, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        hidden1 = 40
        hidden2 = 10

        self.Lin1 = torch.nn.Linear(self.input_dim,hidden1)
        self.ReL = torch.nn.ReLU()
        #self.ReLU = nn.Sigmoid()
        self.Lin2 = torch.nn.Linear(hidden1, hidden2)
        self.Linout = torch.nn.Linear(hidden2, self.output_dim)
        
    def forward(self, x_c, y_c):

      Nc = randomiser(1, range(3, 37)[0] 
      x_c, y_c = context_indexer(Nc, x, y)                                           
      x_c = x_c.view(-1, Nc)
      y_c = y_c.view(-1, Nc)
      pairs = torch.stack([x_c, y_c],2)
      out = self.Lin1(pairs)
      out = self.ReL(out)
      out = self.Linout(out)
      r_c = torch.mean(out, dim=1)
      return out

code to run the encoder

encode=Enc(input_dim=2, output_dim=4)
out = encode(x,y)

The error , it only happens when I include the code for Nc