// Then branch gets the data and calls the reduce_function.
  auto& then_branch = dataset_if.getThenBranch();
  then_branch.push_back(new Block);
  builder.setInsertionPointToEnd(&then_branch.front());
  // Add iterator operational data access inside if.
  auto get_value = builder.create<TF::OptionalGetValueOp>(loc, dataset_types,
                                                          get_next.getResult());

    AddNodeAttr("function", function, &def);

    for (Node* node : nodes_to_remove) {
      VLOG(2) << "Deleting node " << node->DebugString();
      // Ensure that we do not attempt to add control edges to nodes that are
      // deleted.
      control_inputs.erase(node);
      control_outputs.erase(node);
      graph->RemoveNode(node);
    }

// CHECK:           %[[VAL_3:.*]] = tfl.mul(%[[VAL_1]], %[[VAL_2]]) <{fused_activation_function = "NONE"}> : (tensor<3xf32>, tensor<f32>) -> tensor<3xf32>
// CHECK:           %[[VAL_4:.*]] = tfl.add(%[[VAL_0]], %[[VAL_3]]) <{fused_activation_function = "NONE"}> : (tensor<1x384x384x3xf32>, tensor<3xf32>) -> tensor<1x384x384x3xf32>
// CHECK:           return %[[VAL_4]] : tensor<1x384x384x3xf32>
// CHECK:         }

// -----

      });
      return false;
    }
    return true;
  }
};

void FusedKernelMatcherPass::runOnOperation() {
  RewritePatternSet patterns(&getContext());
  auto func = getOperation();
  patterns.add<FuseConv2DBiasAdd, FuseMatMulBiasAdd>(&getContext());

  (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
}

}  // namespace

def CreateTensorScatterNdOp : NativeCodeCall<
    "$_builder.create<TF::ScatterNdOp>($0.getLoc(), $0.getType(), $1, $2, $3)">;

//===----------------------------------------------------------------------===//
// Add op patterns.
//===----------------------------------------------------------------------===//

// We can lower AddOp to AddV2Op, except for string type. AddOp is defined for

// Monitoring Sampler APIs.
// These APIs de-templated monitoring Sampler for swig.

typedef struct TFE_MonitoringSamplerCell TFE_MonitoringSamplerCell;

// Atomically add the value of the cell.
TF_CAPI_EXPORT extern void TFE_MonitoringSamplerCellAdd(
    TFE_MonitoringSamplerCell* cell, double value);

// Retrieves the current value of the cell. The return value is a HistogramProto

    // Insert all ops including nested ops for checking outputs/side effects.
    cluster_op.walk(
        [&](Operation* op) { tail_outside_compiled_ops_set.insert(op); });

    // Only add top level ops to output vector.
    tail_outside_compiled_ops->push_back(&cluster_op);
  }

  *cluster_results = cluster_results_set.takeVector();
}

    return success();
  }
};

void SessionInitializerOp::getCanonicalizationPatterns(
    RewritePatternSet &results, MLIRContext *context) {
  results.add<OptimizeSessionInitializerPattern>(context);
}

SmallVector<StringRef, 2> GetSessionInitializerExportedName(ModuleOp op) {
  auto session_initializer_op = GetSessionInitializerOp(op);

        "tf_device.cluster"() ({
          %group = "tf.Const"() <{value = dense<[[0, 1]]> : tensor<1x2xi32>}> : () -> tensor<1x2xi32>
          %arg1_reduced = "tf.XlaAllReduce"(%arg1_id, %group) <{mode = "CrossReplica", reduce_op = "Add"}> : (tensor<f32>, tensor<1x2xi32>) -> tensor<f32>
          "tf.OpA"(%arg1_reduced) : (tensor<f32>) -> ()
          tf_device.return
        }) : () -> ()
        tf_device.return
      }
      return
    }

        TypeID::get<TF::XlaSpmdFullToShardShapeOp>(),
        TypeID::get<TF::XlaSpmdShardToFullShapeOp>(),
        TypeID::get<TF::XlaSvdOp>(),
    };

    // Add the ops from the TPUEmbeddingOpsRegistry.
    for (auto op_type_id :
         TF::TPUEmbeddingOpsRegistry::Global().GetOpsTypeIds()) {
      ops_set->insert(op_type_id);
    }
    return ops_set;
  }();