%8 = "tfl.mul"(%2, %3) {fused_activation_function = "NONE"} : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi32>

  func.return %5, %6, %7, %8 : tensor<i32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>
}

// CHECK-LABEL: @add_dense_splat_int
func.func @add_dense_splat_int() -> tensor<4xi32> {
  %0 = arith.constant dense<[-10, -1, 42, 100]> : tensor<4xi32>

//
// Examples:
//   * If `xla_gather_op_output_type` == tensor<*xf32>, then it returns:
//     tensor<*xf32>.
//   * If `xla_gather_op_output_type` == tensor<3x5xi32> and `collapsed_dims` ==
//     {0}, then it returns: tensor<1x3x5xi32>.
//   * If `xla_gather_op_output_type` == tensor<3x5xf32> and `collapsed_dims` ==
//     {1, 3}, then it returns: tensor<3x1x5x1xf32>.
Type GetSliceOpOutputType(Type xla_gather_op_output_type,

// CHECK-NEXT:      %6 = "tfl.pseudo_const"() <{value = dense<[-1, -1, 0]> : tensor<3xi32>}> : () -> tensor<3xi32>
// CHECK-NEXT:      %7 = "tfl.pseudo_const"() <{value = dense<1> : tensor<i32>}> : () -> tensor<i32>
// CHECK-NEXT:      %8 = "tfl.unsorted_segment_prod"(%4, %5, %7) : (tensor<3xi32>, tensor<3xi32>, tensor<i32>) -> tensor<1xi32>

  func.return %2 : tensor<1x128x32xf32>

// CHECK-DAG:  [[VAL_1:%.*]] = arith.constant dense<0> : tensor<3xi32>
// CHECK-DAG:  [[VAL_2:%.*]] = arith.constant dense<[1, 128, 32]> : tensor<3xi32>
// CHECK:  [[VAL_3:%.*]] = "tfl.slice"(%arg0, [[VAL_1]], [[VAL_2]]) : (tensor<4x128x32xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<1x128x32xf32>
}

// -----

// CHECK-LABEL: @WhileCanonicalizeBug

  // CHECK-NEXT: %[[UPDATE_SLICE:.*]] = "tf.Reshape"(%[[PUSH_VAL]], %[[UPDATE_SHAPE]]) : (tensor<2xi32>, tensor<2xi32>) -> tensor<1x2xi32>
  // CHECK-NEXT: %[[ZERO_INDS:.*]] = "tf.Const"() <{value = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
  // CHECK-NEXT: %[[CONCAT_DIM:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>

    "tf.AssignVariableOp"(%arg0, %partitioned_output#0) : (tensor<!tf_type.resource<tensor<3x2xf32>>>, tensor<3x2xf32>) -> ()
    "tf.AssignVariableOp"(%arg1, %partitioned_output#1) : (tensor<!tf_type.resource<tensor<3x2xf32>>>, tensor<3x2xf32>) -> ()
    func.return
  }
  func.func @computation(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
    func.return %arg0: tensor<3x4xf32>
  }
}

// -----

  %2 = "tfl.dequantize"(%arg0) : (tensor<*x!quant.uniform<u8:f32, 1.0>>) -> tensor<*xf32>
  %3 = "tfl.dequantize"(%arg1) : (tensor<?x?x4x!quant.uniform<u8:f32, 1.0>>) -> tensor<?x?x4xf32>
  %5 = "tfl.shape"(%2) : (tensor<*xf32>) -> tensor<?xi32>
  %6 = "tfl.shape"(%3) : (tensor<?x?x4xf32>) -> tensor<3xi32>
  func.return %5, %6 : tensor<?xi32>, tensor<3xi32>

    %3 = stablehlo.concatenate %2, %cst_0, %cst_1, %cst_2, dim = 0 : (tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<4xi32>
    %4 = stablehlo.dynamic_broadcast_in_dim %arg2, %3, dims = [0, 1, 2, 3] : (tensor<1x1x1x2xf32>, tensor<4xi32>) -> tensor<?x3x4x2xf32>
    %5 = stablehlo.add %0, %4 : tensor<?x3x4x2xf32>
    return %5 : tensor<?x3x4x2xf32>
  }
}

// -----

// Tests that a float `stablehlo.reshape` is not converted to `tfl.reshape`.

func.func @reshape_float(%arg0: tensor<2x3x4xf32>) -> tensor<6x4xf32> {
  %0 = stablehlo.reshape %arg0 : (tensor<2x3x4xf32>) -> tensor<6x4xf32>
  return %0 : tensor<6x4xf32>
}
// CHECK-LABEL: reshape_float
// CHECK-NOT: tfl.reshape
// CHECK: stablehlo.reshape

// -----

  %19 = "tf.StringLength"(%arg0) {device = "", unit = "BYTE"} : (tensor<1x!tf_type.string>) -> tensor<1xi32>
  %20 = "tf.ExpandDims"(%19, %7) {device = ""} : (tensor<1xi32>, tensor<i32>) -> tensor<1x1xi32>
  %21 = "tf.Cast"(%20) {Truncate = false, device = ""} : (tensor<1x1xi32>) -> tensor<1x1xi64>
  %22 = "tf.Reshape"(%21, %12) {device = ""} : (tensor<1x1xi64>, tensor<1xi64>) -> tensor<1xi64>