if (!control_node.GetBody().args_empty())
    return control_node.emitOpError() << "expects body without any arguments";

  Operation& yield = control_node.GetBody().back();
  if (!isa<YieldOp>(yield))
    return yield.emitOpError()
           << "invalid TFL.control_node terminator, yield expected";

  // Ensure that the terminator's operands and the control_node results match in
  // types.
  const int result_count =

  def _simple_model_data_gen(self) -> repr_dataset.RepresentativeDataset:
    """Creates an interable of representative samples.

    Yields:
      Representative samples, which is basically a mapping of: input key ->
      input value.
    """
    for _ in range(8):
      yield {
          'input_tensor': ops.convert_to_tensor(
              np.random.uniform(low=0, high=150, size=(1, 4)).astype('f4')
          ),

    "tf.IfRegion"(%0) ( {
      %2 = "tf.A"() : () -> (tensor<f32>)
      "tf.Yield"() : () -> ()
      }, {
      "tf.Yield"() : () -> ()
     }) { is_stateless = true } : (tensor<i1>) -> ()
    "tf.IfRegion"(%0) ( {
      %2 = "tf.B"() : () -> (tensor<f32>)
      "tf.Yield"() : () -> ()
      }, {
      "tf.Yield"() : () -> ()
      }) { is_stateless = true } : (tensor<i1>) -> ()
    ```

    // if they are yielded they always have to be forwarded 1:1.
    auto forwarded_operands = cond_yield->getOperands().drop_front(1);
    for (auto [arg, yield] :
         llvm::zip(op.getCond().getArguments(), forwarded_operands)) {
      if (arg != yield) {
        return op.emitOpError()
               << "arguments on condition block aren't forwarded to yield";
      }
    }
  }

  // CHECK:   %[[MUL:.*]] = "tf.Mul"(%[[ARG0]], %[[ARG1]])
  // CHECK:   "tf.Yield"(%[[MUL]]) : (tensor<f32>)
  // CHECK:  },  {
  // CHECK:    %[[SUB:.*]] = "tf.Sub"(%[[ARG0]], %[[ARG1]])
  // CHECK:    "tf.Yield"(%[[SUB]]) : (tensor<f32>)
  // CHECK:  },  {
  // CHECK:    %[[ADD:.*]] = "tf.AddV2"(%[[ARG0]], %[[ARG1]])
  // CHECK:    "tf.Yield"(%[[ADD]]) : (tensor<f32>)

      %x_true = ...
      %y_true = ...
      tfl.yield %x_true, %y_true : tensor<f32>, tensor<f32>
    } else {
      %x_false = ...
      %y_false = ...
      tfl.yield %x_false, %y_false : tensor<f32>, tensor<f32>
    }
    ```

    `tfl.if` regions are always terminated with "tfl.yield". If "tfl.if"
    defines no values, the "tfl.yield" can be left out, and will be inserted

      // each region and the use of TensorList returned using YieldOp.
      if (auto while_region = llvm::dyn_cast<WhileRegionOp>(use.getOwner())) {
        DCOMMENT("\tTL WhileRegion");
        for (auto branch : while_region.getRegions())
          add_to_worklist(branch->getArgument(use.getOperandNumber()));
        continue;
      }
      if (auto yield = llvm::dyn_cast<YieldOp>(use.getOwner())) {

  // Inline the region in place of the IfRegion op, and forward the yield
  // inputs to the IfRegion op results. This is possible only if the yield
  // types match the result types.
  auto yield = cast<YieldOp>(region.front().getTerminator());
  auto updated_results = llvm::to_vector<4>(yield.getOperands());

  // If the yield types do not match the IfRegion result types, add appropriate
  // casts.

<pre>
(*T).Mp
</pre>

<p>
yields a function value representing <code>Mp</code> with signature
</p>

<pre>
func(tp *T, f float32) float32
</pre>

<p>
For a method with a value receiver, one can derive a function
with an explicit pointer receiver, so
</p>

<pre>
(*T).Mv
</pre>

<p>
yields a function value representing <code>Mv</code> with signature

    %1 = func.call @WhileOp_cond(%arg2, %arg3) : (tensor<*xi32>, tensor<*xf32>) -> tensor<i1>
    "tfl.yield"(%1) : (tensor<i1>) -> ()
  },  {
  ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
    %1:2 = func.call @WhileOp_body(%arg2, %arg3) : (tensor<*xi32>, tensor<*xf32>) -> (tensor<*xi32>, tensor<*xf32>)
    "tfl.yield"(%1#0, %1#1) : (tensor<*xi32>, tensor<*xf32>) -> ()
  }) : (tensor<i32>, tensor<1xf32>) -> (tensor<i32>)