// CHECK-LABEL: func @__forward_call_369
// CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain
// CHECK-SAME: [[arg1:%.*]]: !tfrt_fallback.tf_tensor {tf._user_specified_name = "inputs"},
// CHECK-SAME: [[arg2:%.*]]: !tfrt_fallback.tf_tensor, [[arg3:%.*]]: !tfrt_fallback.tf_tensor, [[arg4:%.*]]: !tfrt_fallback.tf_tensor, [[arg5:%.*]]: !tfrt_fallback.tf_tensor)
// CHECK-SAME: -> (!tfrt.chain
// CHECK: [[o1:%.*]] = tfrt_fallback_async.const_dense_tensor

  %0:2 = "tfl.while"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
    %1 = func.call @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 @body(%arg2, %arg3) : (tensor<*xi32>, tensor<*xf32>) -> (tensor<*xi32>, tensor<*xf32>)

                    // enable re-resolution
                    org.eclipse.aether.artifact.Artifact art = dep.getArtifact();
                    art = art.setFile(null).setVersion(art.getBaseVersion());
                    dep = dep.setArtifact(art);
                }
                collect.addDependency(dep);
            }
        }

  func.func @inference_Fn_101680(%arg0: tensor<*x!tf_type.string>, %arg1: tensor<*x!tf_type.string>, %arg2: tensor<*xi32>, %arg3: tensor<*xi32>) -> tensor<*xi32> {

loc(\22Reshape\22)\0A#loc9 = loc(fused[#loc1, #loc2, #loc3])\0Amodule {\0A  func.func @host_func(%arg0: tensor<3360x?xi32> loc(fused[#loc1, #loc2, #loc3]), %arg1: tensor<3xi32> loc(fused[#loc1, #loc2, #loc3]), %arg2: tensor<i32> loc(fused[#loc1, #loc2, #loc3]), %arg3: tensor<2xi32> loc(fused[#loc1, #loc2, #loc3])) -> (tensor<1x1120x?xi32>, tensor<1x1120x?xi32>, tensor<1120x?xi32>, tensor<2xi32>) {\0A    %0 = \22tf.Reshape\22(%arg0, %arg1) {_xla_outside_compilation = \220\22} : (tensor<3360x?xi32>, tensor<3xi32>)...

  func.return %8 : tensor<*xf32>
}

func.func @lstmcellsimple(%arg0: tensor<1x?xf32>, %arg1: tensor<3x4xf32>, %arg2: tensor<2xf32>, %arg3: tensor<1x3xf32>, %arg4: tensor<?xf32>) -> tensor<1x?xf32> attributes  {tf._implements = "LSTMCellSimple", tf._reference = "mlir"} {
    %0 = "tf.BatchMatMulV2"(%arg3, %arg1) {adj_x = false, adj_y = false} : (tensor<1x3xf32>, tensor<3x4xf32>) -> tensor<1x4xf32>

  %max_size = mhlo.constant dense<2> : tensor<i32>
  // NO_FALLBACK: tf.NonMaxSuppressionV4
  // SUPPORTED_FALLBACK_DEVICE-NOT: tf.NonMaxSuppressionV4
  // UNSPECIFIED_FALLBACK_DEVICE: tf.NonMaxSuppressionV4
  // UNSUPPORTED_FALLBACK_DEVICE:  tf.NonMaxSuppressionV4

		{name: "SUBLmodifyidx4", argLength: 4, reg: gpstoreidx, asm: "SUBL", aux: "SymOff", typ: "Mem", clobberFlags: true, symEffect: "Read,Write"}, // *(arg0+arg1*4+auxint+aux) -= arg2, arg3=mem
		{name: "ANDLmodifyidx4", argLength: 4, reg: gpstoreidx, asm: "ANDL", aux: "SymOff", typ: "Mem", clobberFlags: true, symEffect: "Read,Write"}, // *(arg0+arg1*4+auxint+aux) &= arg2, arg3=mem

// CHECK:  %[[BATCHNORM1_a:[^,]+]], {{.*}} = "tf.FusedBatchNormV3"(%[[ADD2]], %[[CONSTANT1]], %[[ARG2]], %[[ARG3]], %[[ARG4]])
// CHECK:  %[[BATCHNORM1_b:[^,]+]], {{.*}} = "tf.FusedBatchNormV3"(%[[BATCHNORM1_a]], %[[CONSTANT1]], %[[ARG2]], %[[ARG3]], %[[ARG4]])
// CHECK:  "tf.FusedBatchNormV3"(%[[BATCHNORM1_b]], %[[ARG1]], %[[ARG2]], %[[ARG3]], %[[ARG4]])
}


func.func @batchNormWithGlobalNormalization(

// WRAPPED-NEXT:   tf_executor.island wraps "tfl.custom_tf"
// WRAPPED-NEXT:     ^bb0(%arg1: tensor<*xf32>, %arg2: tensor<186xf32>, %arg3: tensor<186xf32>):
// WRAPPED-NEXT:   %[[fq:.*]] = "tf.FakeQuantWithMinMaxVarsPerChannel"(%arg1, %arg2, %arg3) <{narrow_range = true, num_bits = 8 : i64}> {device = ""} : (tensor<*xf32>, tensor<186xf32>, tensor<186xf32>) -> tensor<*xf32>