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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def minimum_eigen_vector(x, num_steps, learning_rate, vector_prod_fn):
 """Computes eigenvector which corresponds to minimum eigenvalue. Args: x: initial value of eigenvector. num_steps: number of optimization steps. learning_rate: learning rate. vector_prod_fn: function which takes x and returns product H*x. Returns: approximate value of eigenvector. This function finds approximate value of eigenvector of matrix H which corresponds to smallest (by absolute value) eigenvalue of H. It works by solving optimization problem x^{T}*H*x -> min. """

  x = tf.nn.l2_normalize(x)
  for _ in range(num_steps):
    x = eig_one_step(x, learning_rate, vector_prod_fn)
  return x