// CHECK-NEXT:    return %2 : tensor<3x2xf32>
}

func.func @transpose_3d(%arg0: tensor<1x2x3xf32>) -> tensor<3x2x1xf32> {
  %0 = "mhlo.transpose"(%arg0) <{permutation = dense<[2, 1, 0]> : tensor<3xi64>}> : (tensor<1x2x3xf32>) -> tensor<3x2x1xf32>
  func.return %0 : tensor<3x2x1xf32>

// CHECK-LABEL:   transpose_3d

  func.return %1 : tensor<1x2x2x5xf32>

// CHECK: %0 = "tfl.dequantize"(%arg0)
// CHECK: %1 = "tfl.strided_slice"(%0, %arg1, %arg2, %arg3)
// CHECK: %2 = "tfl.quantize"(%1) <{qtype = tensor<1x2x2x5x!quant.uniform<u8:f32, 1.000000e-01>>}> {volatile}
// CHECK: %3 = "tfl.dequantize"(%2)
// CHECK: return %3 : tensor<1x2x2x5xf32>
}

// CHECK-LABEL: QuantizePad

    quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
  } : (tensor<1x2x2x1x!tf_type.qint32>, tensor<f32>, tensor<i32>) -> tensor<1x2x2x1xf32>

  return %output : tensor<1x2x2x1xi32>

  func.return %0 : tensor<2x2xf16>
}

// -----

// CHECK-LABEL: squeeze
func.func @squeeze(%arg0: tensor<1x1x10xf32>) -> tensor<1x10xf32> {
  // CHECK: mhlo.reshape
  %0 = "tf.Squeeze"(%arg0) : (tensor<1x1x10xf32>) -> tensor<1x10xf32>
  func.return %0 : tensor<1x10xf32>
}

// -----

// CHECK-LABEL: squeeze_ranked

    return success();
  }
};

// Fuses Unpack with proceeding Concatenation to Reshape if output type has
// static shape and activation function is none. For example:
//
//   // %input: tensor<1x3x2xf32>
//   %unpack:3 = "tfl.unpack"(%input) {axis = 1 : i32, num = 3 : i32}
//   %res = "tfl.concatenation"(%unpack#0, %unpack#1, %unpack#2)
//        {axis = -1 : i32, fused_activation_function = "NONE"}
//

    }
  }
  return new_arg_type;
}

// Combination of value producer and port of value produced (e.g.,
//   <value result output>:<value in output tensor>,
// so for tf.Const -> tensor<10x20xf32>, [0,2,18] would point to a unique output
// scalar value).
struct ValuePort {
  PointerUnion<Operation*, BlockArgument> producer;
  SmallVector<unsigned int, 2> port;