code: def m_step(X, resp):
      xp = cupy.get_array_module(X)
      nk = xp.sum(resp, axis=0)
      means = xp.dot(resp.T, X) / nk[:, None]
      X2 = xp.dot(resp.T, X * X) / nk[:, None]
      covariances = X2 - means**2
      return nk / len(X), means, covariances
  
file_path: cupy/examples/gmm/gmm.py
imports: import argparse
  import contextlib
  import time
  import matplotlib.pyplot
  import numpy
  from scipy import stats
  import cupy
label_desc: Use image node_id 3 for calling a global function with example usage: m_step(X, resp) and returns: unknown,  means,  covariances