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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 diet_expert(x, hidden_size, params):
 """A two-layer feed-forward network with relu activation on hidden layer. Uses diet variables. Recomputes hidden layer on backprop to save activation memory. Args: x: a Tensor with shape [batch, io_size] hidden_size: an integer params: a diet variable HParams object. Returns: a Tensor with shape [batch, io_size] """


  @fn_with_diet_vars(params)
  def diet_expert_internal(x):
    dim = x.get_shape().as_list()[-1]
    h = tf.layers.dense(x, hidden_size, activation=tf.nn.relu, use_bias=False)
    y = tf.layers.dense(h, dim, use_bias=False)
    y *= tf.rsqrt(tf.to_float(dim * hidden_size))
    return y

  return diet_expert_internal(x)