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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 split_to_discretized_mix_logistic_params(inputs):
 """Splits input tensor into parameters of discretized mixture logistic. Args: inputs: 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. Returns: Tuple of unconstrained mixture probabilities, locations, scales, and coefficient parameters of the distribution. The mixture probability has shape [batch, height, width, num_mixtures]. Other parameters have shape [batch, height, width, num_mixtures, 3]. """

  batch, height, width, output_dim = shape_list(inputs)  # pylint: disable=unbalanced-tuple-unpacking
  num_mixtures = output_dim // 10
  logits, locs, log_scales, coeffs = tf.split(
      inputs,
      num_or_size_splits=[
          num_mixtures, num_mixtures * 3, num_mixtures * 3, num_mixtures * 3
      ],
      axis=-1)
  split_shape = [batch, height, width, num_mixtures, 3]
  locs = tf.reshape(locs, split_shape)
  log_scales = tf.reshape(log_scales, split_shape)
  log_scales = tf.maximum(log_scales, -7.)
  coeffs = tf.reshape(coeffs, split_shape)
  coeffs = tf.tanh(coeffs)
  return logits, locs, log_scales, coeffs