<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def iaf_flow(one_hot_assignments, scale_weights, scale_bias, num_codes, summary=True, name=None):
 """Performs a single IAF flow using scale and normalization transformations. Args: one_hot_assignments: Assignments Tensor with shape [num_samples, batch_size, latent_size, num_codes]. scale_weights: Tensor corresponding to lower triangular matrix used to autoregressively generate scale matrix from assignments. To ensure the lower-triangular matrix has length of latent_size, scale_weights should be a rank-one tensor with size latent_size * (latent_size + 1) / 2. scale_bias: Bias tensor to be added to scale tensor, with shape [latent_size, num_codes]. If scale weights are zero, initialize scale_bias to be log(exp(1.) / 2. - 1) so initial transformation is identity. num_codes: Number of codes in codebook. summary: Whether to save summaries. name: String used for name scope. Returns: flow_output: Transformed one-hot assignments. inverse_log_det_jacobian: Inverse log deteriminant of Jacobian corresponding to transformation. """

  with tf.name_scope(name, default_name="iaf"):
    # Pad the one_hot_assignments by zeroing out the first latent dimension and
    # shifting the rest down by one (and removing the last dimension).
    padded_assignments = tf.pad(
        one_hot_assignments, [[0, 0], [0, 0], [1, 0], [0, 0]])[:, :, :-1, :]
    scale_bijector = tfp.distributions.bijectors.Affine(
        scale_tril=tfp.distributions.fill_triangular(scale_weights))
    scale = scale_bijector.forward(
        tf.transpose(padded_assignments, [0, 1, 3, 2]))
    # Transpose the bijector output since it performs a batch matmul.
    scale = tf.transpose(scale, [0, 1, 3, 2])
    scale = tf.nn.softplus(scale)
    scale = scale + tf.nn.softplus(scale_bias[tf.newaxis, tf.newaxis, ...])
    # Don't need last dimension since the transformation keeps it constant.
    scale = scale[..., :-1]

    z = one_hot_assignments[..., :-1]
    unnormalized_probs = tf.concat([z * scale,
                                    one_hot_assignments[..., -1, tf.newaxis]],
                                   axis=-1)
    normalizer = tf.reduce_sum(unnormalized_probs, axis=-1)
    flow_output = unnormalized_probs / (normalizer[..., tf.newaxis])
    inverse_log_det_jacobian = (-tf.reduce_sum(tf.log(scale), axis=-1)
                                + num_codes * tf.log(normalizer))
    if summary:
      tf.summary.histogram("iaf/scale", tf.reshape(scale, [-1]))
      tf.summary.histogram("iaf/inverse_log_det_jacobian",
                           tf.reshape(inverse_log_det_jacobian, [-1]))
    return flow_output, inverse_log_det_jacobian