code: def compute_mu_uniform(
      *, steps: int, noise_multiplier: float, sample_rate: float
  ) -> float:
      """
      Compute mu from uniform subsampling.
  
      Args:
          steps: Number of steps taken
          noise_multiplier: Noise multiplier (sigma)
          sample_rate: Sample rate
  
      Returns:
          mu
      """
  
      c = sample_rate * np.sqrt(steps)
      return (
          np.sqrt(2)
          * c
          * np.sqrt(
              np.exp(noise_multiplier ** (-2))
              * norm.cdf(1.5 / noise_multiplier)
              + 3 * norm.cdf(-0.5 / noise_multiplier)
              - 2
          )
      )
  
file_path: opacus/opacus/accountants/analysis/gdp.py
imports: import numpy
  from scipy import optimize
  from scipy.stats import norm
label_desc: Use image node_id 1 for calling a global function with example usage: compute_mu_uniform() and returns: unknown