class_docstr: A `tf.linalg.LinearOperator` representing a kernel covariance matrix.
class_docstr_tok: ["A","`","tf.linalg.LinearOperator","`","representing","a","kernel","covariance","matrix","."]
code: def _matmul(self, x, adjoint=False, adjoint_arg=False):
      x1, x2 = self._x1_x2()
      if self._num_matmul_parts is None or prefer_static.equal(
          self._num_matmul_parts, 1
      ):
          return tf.matmul(
              self._kernel().matrix(x1, x2),
              x,
              adjoint_a=adjoint,
              adjoint_b=adjoint_arg,
          )
  
      if adjoint or adjoint_arg:
          raise NotImplementedError(
              "`adjoint`, `adjoint_arg` NYI when `num_matmul_parts` specified."
          )
  
      return _chunked_matmul(
          kernel_fn=self.kernel_fn,
          kernel_args=self.kernel_args,
          x1=x1,
          x2=x2,
          x=x,
          num_matmul_parts=self._num_matmul_parts,
          operator_shape=self.shape_tensor(),
      )
  
imports: import functools
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow.python.ops import control_flow_util
label_desc: Use image node_id 9 for calling the LinearOperatorPSDKernel obj's underlying member method code with example usage: obj._matmul(x, adjoint, adjoint_arg) and returns: _chunked_matmul, tf