(num_rows, num_columns, **kwargs)
return tf.concat(ans, axis=0)
Returns a batch of linearly interpolated latent vectors.

  Given two randomly generated latent vector za and zb, it can generate
  a row of `num_columns` interpolated latent vectors, i.e.
  [..., za + (zb - za) * i / (num_columns - 1), ...] where
  i = 0, 1, ..., `num_columns` - 1.

  This function produces `num_rows` such rows and returns a (flattened)
  batch of latent vectors with batch size `num_rows * num_columns`.

  Args:
    num_rows: An integer. Number of rows of interpolated latent vectors.
    num_columns: An integer. Number of interpolated latent vectors in each row.
    **kwargs: A dictionary of
        'latent_vector_size': An integer of latent vector size.

  Returns:
    A `Tensor` of shape `[num_rows * num_columns, latent_vector_size]`.
Returns a batch of linearly interpolated latent vectors.
Returns a batch of linearly interpolated latent vectors .
def make_interpolated_latent_vectors(num_rows, num_columns, **kwargs):
  """Returns a batch of linearly interpolated latent vectors.

  Given two randomly generated latent vector za and zb, it can generate
  a row of `num_columns` interpolated latent vectors, i.e.
  [..., za + (zb - za) * i / (num_columns - 1), ...] where
  i = 0, 1, ..., `num_columns` - 1.

  This function produces `num_rows` such rows and returns a (flattened)
  batch of latent vectors with batch size `num_rows * num_columns`.

  Args:
    num_rows: An integer. Number of rows of interpolated latent vectors.
    num_columns: An integer. Number of interpolated latent vectors in each row.
    **kwargs: A dictionary of
        'latent_vector_size': An integer of latent vector size.

  Returns:
    A `Tensor` of shape `[num_rows * num_columns, latent_vector_size]`.
  """
  ans = []
  for _ in range(num_rows):
    z = tf.random_normal([2, kwargs['latent_vector_size']])
    r = tf.reshape(
        tf.to_float(tf.range(num_columns)) / (num_columns - 1), [-1, 1])
    dz = z[1] - z[0]
    ans.append(z[0] + tf.stack([dz] * num_columns) * r)
  return tf.concat(ans, axis=0)
def make_interpolated_latent_vectors ( num_rows , num_columns , * * kwargs ) : ans = [ ] for _ in range ( num_rows ) : z = tf . random_normal ( [ 2 , kwargs [ 'latent_vector_size' ] ] ) r = tf . reshape ( tf . to_float ( tf . range ( num_columns ) ) / ( num_columns - 1 ) , [ - 1 , 1 ] ) dz = z [ 1 ] - z [ 0 ] ans . append ( z [ 0 ] + tf . stack ( [ dz ] * num_columns ) * r ) return tf . concat ( ans , axis = 0 )