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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 multiplicative_jitter(x, epsilon=1e-2):
 """Multiply values by a random number between 1-epsilon and 1+epsilon. Makes models more resilient to rounding errors introduced by bfloat16. This seems particularly important for logits. Args: x: a mtf.Tensor epsilon: a floating point value Returns: a mtf.Tensor with the same type and shape as x. """

  if epsilon == 0:
    return x
  return x * mtf.random_uniform(
      x.mesh, x.shape, minval=1.0 - epsilon, maxval=1.0+epsilon, dtype=x.dtype)