code: def generate_covariates(n, d, n_binary=0, p=0.5):
      """
      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)
      """
      # 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
  
imports: import numpy
  import pandas
  from scipy import stats
  from scipy.optimize import newton
  from scipy.integrate import cumtrapz
label_desc: Use image node_id 9 for calling a global function with example usage: generate_covariates(n, d, n_binary, p) and returns: covariates