}

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>

}

func.func @broadcast_args_i64(%arg0: tensor<3xi64>, %arg1: tensor<1xi64>) -> tensor<3xi64> {
  %0 = "tf.BroadcastArgs"(%arg0, %arg1) : (tensor<3xi64>, tensor<1xi64>) -> tensor<3xi64>
  func.return %0 : tensor<3xi64>

// CHECK-LABEL:broadcast_args_i64
// CHECK:  "tfl.broadcast_args"(%arg0, %arg1) : (tensor<3xi64>, tensor<1xi64>) -> tensor<3xi64>
}

// CHECK-DAG:       %[[VAL_2:.*]] = "tf.Const"() <{value = dense<[1, 0]> : tensor<2xi64>}> : () -> tensor<2xi64>
// CHECK-DAG:       %[[VAL_3:.*]] = "tf.Const"() <{value = dense<[1, 0]> : tensor<2xi64>}> : () -> tensor<2xi64>
// CHECK:           %[[VAL_4:.*]] = "tf.Transpose"(%[[VAL_0]], %[[VAL_3]]) : (tensor<2x3xf32>, tensor<2xi64>) -> tensor<3x2xf32>

  %2 = mhlo.constant dense<3.40282347E+38> : tensor<f32>
  %3 = "mhlo.broadcast_in_dim"(%2) <{broadcast_dimensions = dense<> : tensor<0xi64>}> : (tensor<f32>) -> tensor<2xf32>
  %4 = mhlo.constant dense<3.000000e+00> : tensor<f32>
  %5 = "mhlo.broadcast_in_dim"(%4) <{broadcast_dimensions = dense<> : tensor<0xi64>}> : (tensor<f32>) -> tensor<2xf32>

  func.return %3 : tensor<2xi64>
  // CHECK: %[[CST:.*]] = "tf.Const"() <{value = dense<[1, 2]> : tensor<2xi64>}> : () -> tensor<2xi64>
  // CHECK: return %[[CST]]
}

// CHECK-LABEL: testFoldStridedSliceShapeDynamicOutput
func.func @testFoldStridedSliceShapeDynamicOutput(%arg0: tensor<?x1x2x?xf32>) -> (tensor<?xi32>) {

// CHECK: %[[XLA_CALL_MODULE:.+]] = "tf.XlaCallModule"(%[[CUSTOM_AGGREGATOR_0]], %[[CST]])
// CHECK-SAME: _entry_function = @composite_dot_general_fn_1, _original_entry_function = "composite_dot_general_fn_1"

    %1 = "tf.RngReadAndSkip"(%resource, %alg, %delta) : (tensor<!tf_type.resource<tensor<3xi64>>>, tensor<i32>, tensor<ui64>) -> tensor<3xi64>
    tf_device.return %1 : tensor<3xi64>
  }) : () -> tensor<3xi64>
  func.return %0 : tensor<3xi64>
}

// Test that ordering token resource of tf.CollectiveReduceV2 op is removed.
// CHECK-LABEL: func @decompose_collective_reduce_v2_op

  %0 = "tfl.shape"(%arg1) : (tensor<8x7x6x5x?x3x2x1xf32>) -> tensor<8xi64>
  %1 = "tfl.shape"(%arg2) : (tensor<?x3x2x1xf32>) -> tensor<4xi64>
  %2 = "tfl.broadcast_args"(%0, %1) : (tensor<8xi64>, tensor<4xi64>) -> tensor<8xi64>
  %3 = "tfl.shape"(%arg0) : (tensor<8x7x6x5x?x3x2x1xi1>) -> tensor<8xi64>
  %4 = "tfl.broadcast_args"(%2, %3) : (tensor<8xi64>, tensor<8xi64>) -> tensor<8xi64>

    %0 = "tf.XlaCallModule"(%output, %cst) <{Sout = [#tf_type.shape<10x1x3>], dim_args_spec = [], disabled_checks = [], function_list = [], has_token_input_output = false, module = "", platforms = ["CPU"], version = 9 : i64}> {_entry_function...

// CHECK: %[[BITCAST2:.+]] = "tfl.bitcast"(%[[ARG2]]) : (tensor<i64>) -> tensor<1xi64>
// CHECK: %[[MIN2:.+]] = "tfl.minimum"(%[[BITCAST2]], %[[MAX2]]) : (tensor<1xi64>, tensor<1xi64>) -> tensor<1xi64>
// CHECK: %[[CONCAT:.+]] = "tfl.concatenation"(%[[MIN1]], %[[MIN2]]) <{axis = 0 : i32, fused_activation_function = "NONE"}> : (tensor<1xi64>, tensor<1xi64>) -> tensor<2xi64>