class_docstr: A version of the Separable kernel that's inefficient.
code: def _matrix_over_all_tasks(self, x1, x2):
      # Because the kernel computations are independent of task,
      # we can use a Kronecker product of an identity matrix.
      base_kernel_matrix = tf.linalg.LinearOperatorFullMatrix(
          self.base_kernel.matrix(x1, x2)
      )
      operator = tf.linalg.LinearOperatorKronecker(
          [base_kernel_matrix, self._task_kernel_matrix_linop]
      )
      return tf.linalg.LinearOperatorFullMatrix(operator.to_dense())
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gaussian_process
  from tensorflow_probability.python.experimental.distributions import multitask_gaussian_process
  from tensorflow_probability.python.experimental.distributions import multitask_gaussian_process_regression_model
  from tensorflow_probability.python.experimental.psd_kernels import multitask_kernel
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
  from tensorflow_probability.python.math.psd_kernels.internal import test_util
label_desc: Use image node_id 5 for calling the InefficientSeparable obj's underlying member method code with example usage: obj._matrix_over_all_tasks(x1, x2) and returns: tf