%indices = "tf.Const"() {value = dense<[0, 1]> : tensor<2xi32>} : () -> tensor<2xi32>
    %axis = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<i32>
    %1 = "tf.GatherV2"(%0, %indices, %axis) {batch_dims = 0 : i64, device = ""} : (tensor<3xi32>, tensor<2xi32>, tensor<i32>) -> tensor<2xi32>

    %last_dim = "tf.Const"() {value = dense<7> : tensor<1xi32>} : () -> tensor<1xi32>

  func.return %0 : tensor<8xui8>
}

// CHECK-LABEL:   func @torch_index_select(
// CHECK:       %[[AXIS:.+]] = "tf.Const"() <{value = dense<0> : tensor<i64>}> : () -> tensor<i64>
// CHECK:       %[[RES:.+]] = "tf.GatherV2"(%arg0, %arg1, %[[AXIS]]) <{batch_dims = 0 : i64}>
// CHECK:       return %[[RES]]

func.func @torch_index_select(%arg0: tensor<2x1xf32>, %arg1: tensor<2xi32>) -> tensor<2x1xf32> {

  );

  TF_DerivedOperandTypeAttr Tindices = TF_DerivedOperandTypeAttr<1>;
  TF_DerivedOperandTypeAttr Tparams = TF_DerivedOperandTypeAttr<0>;
}

def TF_GatherV2Op : TF_Op<"GatherV2", [Pure]> {
  let summary = [{
Gather slices from `params` axis `axis` according to `indices`.
  }];

  let description = [{
`indices` must be an integer tensor of any dimension (usually 0-D or 1-D).