[Python] Conditional expectation given two stochastic processes

I would like to compute the process Z_t=E[X_T|W_t] where W is a brownian motion (the cumulative sum of white noise) and X_t=np.sqrt(np.abs(W_t)). In Python these are

# Parameters
T = 1.0 # final time
N = 1000 # number of time steps
dt = T / N
t = np.linspace(0, T, N+1)
seed = 12345

# Initialize Brownian motion
rng = np.random.default_rng(seed)
W = np.zeros(N+1)

# Simulate W_t
for k in range(N):
dW = rng.normal(0, np.sqrt(dt))
W[k+1] = W[k] + dW

# Compute sqrt of absolute value
X= np.sqrt(np.abs(W))


But for Z I do not understand how to compute it.

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