%0 = "tfl.pseudo_qconst"() {qtype = tensor<128x!quant.uniform<i32:f32, 0.7>>, value = dense<0> : tensor<128xi32>} : () -> tensor<128x!quant.uniform<i32:f32, 0.7>>

  %1 = "tfl.pseudo_qconst"() {qtype = tensor<128x!quant.uniform<i32:f32, 0.7>>, value = dense<0> : tensor<128xi32>} : () -> tensor<128x!quant.uniform<i32:f32, 0.7>>

  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<1x2xi32>} : () -> tensor<1x2xi32>
  %1 = "tfl.pad"(%arg0, %0) : (tensor<15600xf32>, tensor<1x2xi32>) -> tensor<15600xf32>
  func.return %1 : tensor<15600xf32>
  // CHECK:  return %arg0
}

func.func @ConstFoldPadV2(%arg0: tensor<15600xf32>) -> tensor<15600xf32> {
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<1x2xi32>} : () -> tensor<1x2xi32>

// -----

func.func @testConcatInvalidOperandRankGreater(%arg0: tensor<1x1x2xi32>, %arg1: tensor<1x1x2xi32>) -> tensor<2x2xi32> {
  // expected-error @+1 {{'tfl.concatenation' op rank of operand #0 must be equal to rank of output, expected 2, got 3}}
  %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<1x1x2xi32>, tensor<1x1x2xi32>) -> tensor<2x2xi32>
  func.return %0 : tensor<2x2xi32>
}

func.func @scatter_nd_add(%arg0: tensor<7xi64>, %arg1: tensor<1x1xi32>, %arg2: tensor<1xi64>) -> tensor<7xi64> {
  %0 = "tf.TensorScatterAdd"(%arg0, %arg1, %arg2) : (tensor<7xi64>, tensor<1x1xi32>, tensor<1xi64>) -> tensor<7xi64>
  func.return %0 : tensor<7xi64>

  // CHECK-LABEL: scatter_nd_add
  // CHECK:  %[[GATHER:.*]] = "tf.GatherNd"(%arg0, %arg1) <{bad_indices_policy = ""}> : (tensor<7xi64>, tensor<1x1xi32>) -> tensor<1xi64>

        }) : () -> tensor<f32>
        "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
        }) : () -> ()

  // CHECK:  %5 = "tfl.arg_max"(%arg0, %cst) : (tensor<1x32x1xf32>, tensor<1xi32>) -> tensor<1x1xi32>
  // CHECK:  return %4, %5 : tensor<1x1xf32>, tensor<1x1xi32>
}

// CHECK-LABEL:   func @convert_pytorch_argmax
func.func @convert_pytorch_argmax(%arg0: tensor<1x9xi32>) -> tensor<1xi32> {
  %0 = mhlo.constant dense<0> : tensor<i32>

  %0 = "tfl.pseudo_qconst"() {qtype = tensor<128x!quant.uniform<i32:f32, 0.7>>, value = dense<0> : tensor<128xi32>} : () -> tensor<128x!quant.uniform<i32:f32, 0.7>>

  %0 = "tf.Const"() {value = dense<1> : tensor<1x10xi32>} : () -> tensor<1x10xi32>
  %1 = "tf.Const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<i32>
  func.return %0, %1 : tensor<1x10xi32>, tensor<i32>
}

// CHECK: %[[v0:.*]] = "tf.Transpose"(%arg0, %[[cst]]) : (tensor<?x?x8x128xf32>, tensor<4xi32>) -> tensor<?x8x?x128xf32>
// CHECK: %[[v1:.*]] = "tf.Transpose"(%arg1, %[[cst_0]]) : (tensor<1x?x8x128xf32>, tensor<4xi32>) -> tensor<1x8x128x?xf32>
// CHECK: %[[v3:.*]] = "tf.BatchMatMulV2"(%0, %1) <{adj_x = false, adj_y = false}> : (tensor<?x8x?x128xf32>, tensor<1x8x128x?xf32>) -> tensor<?x8x?x?xf32>