code: def generate_covariates(n, d, n_binary=0, p=0.5):
      
      # pylint: disable=chained-comparison
      assert n_binary >= 0 and n_binary <= d, "binary must be between 0 and d"
      covariates = np.zeros((n, d + 1))
      covariates[:, : d - n_binary] = random.exponential(1, size=(n, d - n_binary))
      covariates[:, d - n_binary : -1] = random.binomial(1, p, size=(n, n_binary))
      covariates[:, -1] = np.ones(n)
      return covariates
docs: n: the number of instances, integer
      d: the dimension of the covarites, integer
      binary: a float between 0 and d the represents the binary covariates
      p: in binary, the probability of 1
  
      returns (n, d+1)