code: def _random_df_loc_and_scale(
      self, batch_shape, matrix_shape, seed_stream=None
  ):
      # This ensures covariance is positive def.
      seed_stream = (
          test_util.test_seed_stream()
          if seed_stream is None
          else seed_stream
      )
      row_shape = batch_shape + [matrix_shape[0], matrix_shape[0]]
      col_shape = batch_shape + [matrix_shape[1], matrix_shape[1]]
      scale_row = tf.linalg.LinearOperatorLowerTriangular(
          self._random_tril_matrix(row_shape, seed_stream()),
          is_non_singular=True,
      )
      scale_col = tf.linalg.LinearOperatorLowerTriangular(
          self._random_tril_matrix(col_shape, seed_stream()),
          is_non_singular=True,
      )
      loc = (
          tf.math.abs(
              tf.random.normal(
                  shape=batch_shape + matrix_shape,
                  stddev=2.0,
                  seed=seed_stream(),
                  dtype=self.dtype,
              )
          )
          + 1.0
      )
      df = (
          tf.math.abs(
              tf.random.normal(
                  shape=batch_shape,
                  seed=seed_stream(),
                  dtype=self.dtype,
              )
          )
          + 5.0
      )
      return df, loc, scale_row, scale_col
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.bijectors import transform_diagonal
  from tensorflow_probability.python.distributions import matrix_t_linear_operator
  from tensorflow_probability.python.distributions import multivariate_student_t
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 2 for calling the _MatrixTTest obj's underlying member method code with example usage: obj._random_df_loc_and_scale(batch_shape, matrix_shape, seed_stream) and returns: df,  loc,  scale_row,  scale_col