%cst = arith.constant dense<[2, 100]> : tensor<2xi64>
    %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<100xf32>, tensor<100xf32>) -> tensor<200xf32>
    %1 = "tfl.reshape"(%0, %cst) : (tensor<200xf32>, tensor<2xi64>) -> tensor<2x100xf32>
    func.return %1 : tensor<2x100xf32>
  }

  // CHECK-NEXT:   %[[LHS_BCAST:.+]] = "mhlo.dynamic_broadcast_in_dim"(%arg0, %[[RESULT_EXTENTS]]) <{broadcast_dimensions = dense<1> : tensor<1xi64>}>
  // CHECK-NEXT:   %[[RHS_BCAST:.+]] = "mhlo.dynamic_broadcast_in_dim"(%arg1, %[[RESULT_EXTENTS]]) <{broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>}>
  // CHECK-NEXT:   %[[RESULT:.+]] = mhlo.add %[[LHS_BCAST]], %[[RHS_BCAST]] : tensor<?x?xi32>
  // CHECK-NEXT:   shape.assuming_yield %[[RESULT]]

      while (auto cast_op =
                 llvm::dyn_cast_or_null<CastOp>(value.getDefiningOp())) {
        // Consider cast compatibility in case
        //    %cast = "tf.Cast"(%0) : (tensor<2xi64>) -> tensor<2xf32>
        // is skipped.
        if (cast_op.getSrcT() != cast_op.getDstT()) {
          break;
        }
        value = cast_op.getOperand();
      }
      return value;
    };

  %1 = "tfl.shape"(%conv_s) : (tensor<?x26x26x26x16xf32>) -> tensor<5xi64>
  %2 = "tfl.broadcast_args"(%1, %cst) : (tensor<5xi64>, tensor<1xi64>) -> tensor<5xi64>
  %broad1 = "tfl.broadcast_to"(%conv_s, %2) : (tensor<?x26x26x26x16xf32>, tensor<5xi64>) -> tensor<?x26x26x26x16xf32>
  %broad2 = "tfl.broadcast_to"(%b, %2) : (tensor<16xf32>, tensor<5xi64>) -> tensor<?x26x26x26x16xf32>

  // CHECK-DAG: %[[X_NORMED:.+]] = mhlo.multiply %[[X]], %[[MULTIPLIER_BCAST]] : tensor<4x256xf32>
  // CHECK-DAG: %[[RHS_BCAST:.+]] = "mhlo.broadcast_in_dim"(%[[RHS]]) <{broadcast_dimensions = dense<1> : tensor<1xi64>}> : (tensor<256xf32>) -> tensor<4x256xf32>

func.func @Int64SliceBeginSize(%arg0: tensor<4x128x32xf32>) -> tensor<1x128x32xf32> {
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3xi64>} : () -> tensor<3xi64>
  %1 = "tfl.pseudo_const"() {value = dense<[1, 128, 32]> : tensor<3xi64>} : () -> tensor<3xi64>
  %2 = "tfl.slice"(%arg0, %0, %1) : (tensor<4x128x32xf32>, tensor<3xi64>, tensor<3xi64>) -> tensor<1x128x32xf32>
  func.return %2 : tensor<1x128x32xf32>

  %2 = mhlo.constant dense<0.000000e+00> : tensor<f32>

    %4 = "tf.AddV2"(%3, %1) {device = ""} : (tensor<?xi64>, tensor<i64>) -> tensor<?xi64>
    %5 = "tf.LookupTableFindV2"(%0, %arg0, %cst) {device = ""} : (tensor<!tf_type.resource>, tensor<?x!tf_type.string>, tensor<i64>) -> tensor<*xi64>
    return %5 : tensor<*xi64>
  }

// Check that HashTable op is lifted.
// CHECK: func.func private @serving_default

    %3 = "tf.XlaCallModule"(%2#0, %0, %1) {Sout = [#tf_type.shape<1x3>], _entry_function = @composite_dot_general_fn_1, _original_entry_function = "composite_dot_general_fn_1", _tfl_quant_trait = "fully_quantizable", dim_args_spec = [], disabled_checks...

  // CHECK-SAME: <{calibration_methods = [1 : i32, 1 : i32], ids = ["0", "1"], output_file_path = "main_0.pb"}> : (tensor<f32>, tensor<f32>, tensor<0xi64>, tensor<f32>, tensor<f32>, tensor<0xi64>) -> ()
  // CHECK: return