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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 isemhash_bottleneck(x, bottleneck_bits, bottleneck_noise, discretize_warmup_steps, mode, isemhash_noise_dev=0.5, isemhash_mix_prob=0.5):
 """Improved semantic hashing bottleneck."""

  with tf.variable_scope("isemhash_bottleneck"):
    x = tf.layers.dense(x, bottleneck_bits, name="dense")
    y = common_layers.saturating_sigmoid(x)
    if isemhash_noise_dev > 0 and mode == tf.estimator.ModeKeys.TRAIN:
      noise = tf.truncated_normal(
          common_layers.shape_list(x), mean=0.0, stddev=isemhash_noise_dev)
      y = common_layers.saturating_sigmoid(x + noise)
    d = tf.to_float(tf.less(0.5, y)) + y - tf.stop_gradient(y)
    d = 2.0 * d - 1.0  # Move from [0, 1] to [-1, 1].
    if mode == tf.estimator.ModeKeys.TRAIN:  # Flip some bits.
      noise = tf.random_uniform(common_layers.shape_list(x))
      noise = 2.0 * tf.to_float(tf.less(bottleneck_noise, noise)) - 1.0
      d *= noise
      d = common_layers.mix(
          d,
          2.0 * y - 1.0,
          discretize_warmup_steps,
          mode == tf.estimator.ModeKeys.TRAIN,
          max_prob=isemhash_mix_prob)
    return d, 0.0