code: def _unvec(x, matrix_shape):
      # Unvec takes in a (batch) of matrices of shape B1 + [n * k] and returns
      # a (batch) of vectors of shape B1 + [n, k], where n and k are specified
      # by matrix_shape.
      return tf.reshape(
          x, ps.concat([ps.shape(x)[:-1], matrix_shape], axis=0)
      )
  
imports: import functools
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import cholesky_util
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import mvn_linear_operator
  from tensorflow_probability.python.distributions.internal import stochastic_process_util
  from tensorflow_probability.python.experimental.linalg import linear_operator_unitary
  from tensorflow_probability.python.experimental.psd_kernels import multitask_kernel
  from tensorflow_probability.python.internal import batch_shape_lib
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow.python.util import nest
label_desc: Use image node_id 2 for calling a global function with example usage: _unvec(x, matrix_shape) and returns: tf