sym_name = "some_constant",
    type = tensor<1x64xf32>,
    value = dense<42.0> : tensor<1x64xf32>
  } : () -> ()

  // Representation for variables: mutable global tensor.
  // CHECK: tf_saved_model.global_tensor
  "tf_saved_model.global_tensor"() {
    is_mutable,
    tf_saved_model.exported_names = ["some_var", "some.other.name"],
    sym_name = "some_variable",
    type = tensor<?x64xf32>,
    value = dense<42.0> : tensor<1x64xf32>

    %9:4 = "tf.CustomAggregator"(%6) {calibration_method = 1 : i32, id = "1", max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32, num_bins = 0 : i32} : (tensor<1x64xf32>) -> (tensor<1x64xf32>, tensor<f32>, tensor<f32>, tensor<*xi64>)
    return %9#0 : tensor<1x64xf32>
  }

         exponential_avg_factor = 1.0 : f32,
         is_training = false
       }
        : (tensor<1x112x112x64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>)
       -> (tensor<1x112x112x64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>)

  func.return %2#0 : tensor<1x112x112x64xf32>
}

// CHECK-LABEL: func @fold_into_pad_with_extra_uses

  %0 = "tfl.reshape"(%arg0, %shape0) : (tensor<4x4x4xf32>, tensor<2xi32>) -> tensor<16x4xf32>
  %1 = "tfl.reshape"(%0, %shape1) : (tensor<16x4xf32>, tensor<1xi32>) -> tensor<64xf32>
  %2 = "tfl.reshape"(%0, %shape1) : (tensor<16x4xf32>, tensor<1xi32>) -> tensor<64xf32>
  %3 = arith.addf %1, %2 : tensor<64xf32>
  func.return %3 : tensor<64xf32>

// CHECK-LABEL: func @reshape_removeAdjacentWithMultipleUse

  func.return %24 : tensor<1x4xf32>
}

  %0:3 = "tfl.unpack"(%arg0) {axis = 1 : i32, num = 3 : i32} : (tensor<1x3x2xf32>) -> (tensor<1x2xf32>, tensor<1x2xf32>, tensor<1x2xf32>)
  %1 = "tfl.concatenation"(%0#0, %0#1, %0#2) {axis = -1 : i32, fused_activation_function = "NONE"} : (tensor<1x2xf32>, tensor<1x2xf32>, tensor<1x2xf32>) -> tensor<1x6xf32>
  func.return %1 : tensor<1x6xf32>

  // time constant.
  %0 = "tf.ConcatV2"(%arg0, %arg0, %arg0, %arg0, %axis) : (tensor<16x1xf32>, tensor<16x1xf32>, tensor<16x1xf32>, tensor<16x1xf32>, tensor<i32>) -> tensor<16x4xf32>

  // Just to introduce an extra use for %cst.
  %1 = "tf.AddV2"(%axis, %axis) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>

  return %0 : tensor<16x4xf32>
}
// Check that the constant is cloned with same value.

// DISABLE-ALL-DOT-GENERAL: @main
func.func @main(%arg0: tensor<1x1x167xf32>) -> tensor<1x1x64xf32> {
  %0 = stablehlo.constant dense<2.000000e+00> : tensor<167x64xf32>
  %1 = stablehlo.dot_general %arg0, %0, contracting_dims = [2] x [0], precision = [DEFAULT, DEFAULT] : (tensor<1x1x167xf32>, tensor<167x64xf32>) -> tensor<1x1x64xf32>
  return %1 : tensor<1x1x64xf32>
}

// DISABLE-ALL-DOT-GENERAL: %[[CONST:.+]] = stablehlo.constant dense<2.000000e+00>

// CHECK:           }) : (tensor<1x?xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, none, none, none, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<3x1xf32>, tensor<3xf32>, tensor<1x3xf32>, tensor<1x1xf32>, none, none, none, none) -> tensor<1x3xf32>
// CHECK:           [[VAL_50:%.*]] = tensor.cast [[VAL_51:%.*]] : tensor<1x3xf32> to tensor<1x?xf32>

  %5 = "quantfork.stats"(%4) {layerStats = dense<[-56.2916565, 122.922478]> : tensor<2xf32>} : (tensor<1x4xf32>) -> tensor<1x4xf32>
  %6 = "tfl.svdf"(%0, %1, %2, %3, %5) {fused_activation_function = "RELU", rank = 1 : i32} : (tensor<1x3xf32>, tensor<2x3xf32>, tensor<2x1xf32>, tensor<2xf32>, tensor<1x4xf32>) -> tensor<1x2xf32>