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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 sample_from_discretized_mix_logistic(pred, seed=None):
 """Sampling from a discretized mixture of logistics. Args: pred: A [batch, height, width, num_mixtures*10] tensor of floats comprising one unconstrained mixture probability, three means (one per channel), three standard deviations (one per channel), and three coefficients which linearly parameterize dependence across channels. seed: Random seed. Returns: A tensor of shape [batch, height, width, 3] with real intensities scaled between -1 and 1. """


  logits, locs, log_scales, coeffs = split_to_discretized_mix_logistic_params(
      pred)

  # Sample mixture indicator given logits using the gumbel max trick.
  num_mixtures = shape_list(logits)[-1]
  gumbel_noise = -tf.log(-tf.log(
      tf.random_uniform(
          tf.shape(logits), minval=1e-5, maxval=1. - 1e-5, seed=seed)))
  sel = tf.one_hot(
      tf.argmax(logits + gumbel_noise, -1),
      depth=num_mixtures,
      dtype=tf.float32)

  # Select mixture component's parameters.
  sel = tf.expand_dims(sel, -1)
  locs = tf.reduce_sum(locs * sel, 3)
  log_scales = tf.reduce_sum(log_scales * sel, 3)
  coeffs = tf.reduce_sum(coeffs * sel, 3)

  # Sample from 3-D logistic & clip to interval. Note we don't round to the
  # nearest 8-bit value when sampling.
  uniform_noise = tf.random_uniform(
      tf.shape(locs), minval=1e-5, maxval=1. - 1e-5, seed=seed)
  logistic_noise = tf.log(uniform_noise) - tf.log1p(-uniform_noise)
  x = locs + tf.exp(log_scales) * logistic_noise
  x0 = x[..., 0]
  x1 = x[..., 1] + coeffs[..., 0] * x0
  x2 = x[..., 2] + coeffs[..., 1] * x0 + coeffs[..., 2] * x1
  x = tf.stack([x0, x1, x2], axis=-1)
  x = tf.clip_by_value(x, -1., 1.)
  return x