{"tf.Yield"(%if3) : (!tf_variant) -> ()})
        {is_stateless = true} : (tensor<i1>) -> !tf_variant
    %if5 = "tf.IfRegion"(%arg0) ({"tf.Yield"(%if4) : (!tf_variant) -> ()},
                                 {"tf.Yield"(%if4) : (!tf_variant) -> ()})
        {is_stateless = true} : (tensor<i1>) -> !tf_variant
    %if6 = "tf.IfRegion"(%arg0) ({"tf.Yield"(%if5) : (!tf_variant) -> ()},

    "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>) -> ()
    ```

  // 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>)

                    yield.getOperands().begin());
}

mlir::LogicalResult ControlNodeOp::verify() {
  ControlNodeOp control_node = *this;
  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()

      outlier = np.random.uniform(low=0, high=10, size=(1, 3, 4, 3)).astype(
          'f4'
      )
      outlier[0][0][0][0:2] = [-1000, 1000]
      yield {'input_tensor': ops.convert_to_tensor(outlier)}
      for _ in range(10):
        yield {
            'input_tensor': ops.convert_to_tensor(
                np.random.uniform(low=0, high=10, size=(1, 3, 4, 3)).astype(
                    'f4'

  // Allow either the "yield cond" or "yield cond, arg1, ... argN" form,
  // for the yield in the condition block.
  if (cond_yield->getNumOperands() != 1 &&
      cond_yield->getNumOperands() != op.getCond().getArguments().size() + 1)
    return op.emitOpError()
           << "condition should yield a tensor<i1> and forward the arguments";

  auto cond_type =

  // 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.

    %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>)

      %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

        for (auto branch : while_region.getRegions())
          add_to_worklist(branch->getArgument(use.getOperandNumber()));
        continue;
      }
      if (auto yield = llvm::dyn_cast<YieldOp>(use.getOwner())) {
        Operation* parent = yield->getParentOp();
        add_to_worklist(parent->getResult(use.getOperandNumber()));
        continue;
      }
      // TODO(jpienaar): This can be generalized.