%0 = "tfl.pseudo_const"() {value = dense<0.962260901> : tensor<1xf32>} : () -> tensor<1xf32>
    %1 = func.call @func_0_GPU_FLOAT(%arg0, %0) {tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_0"} : (tensor<1x200x200x200xf32>, tensor<1xf32>) -> tensor<1x200x200x200xf32>
    %2 = "tfl.pseudo_const"() {value = dense<0.895973444> : tensor<1xf32>} : () -> tensor<1xf32>

  func.return %1: tensor<1x5xf32>
}

// -----

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<2x2xf32>

// CHECK:  %[[cst:.*]] = "tfl.pseudo_const"(){{.*}}dense<-1.000000e-01> : tensor<2x2xf32>
// CHECK:  return %[[cst]] : tensor<2x2xf32>
}

// CHECK-LABEL: DequantizeAndQuantize
func.func @DequantizeAndQuantize() -> tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>> {

  %0 = "quantfork.stats"(%arg0) {
    layerStats = dense<[0.0, 1.0]> : tensor<2xf32>
  } : (tensor<?x5x5x2xf32>) -> tensor<?x5x5x2xf32>
  %1 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<3x5x5x2xf32>} : () -> tensor<3x5x5x2xf32>
  %2 = "tfl.pseudo_const"() {value = dense<0.000000e+00> : tensor<3xf32>} : () -> tensor<3xf32>
  %3 = "tfl.conv_2d"(%0, %1, %2) {
    dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32,

  %0 = "quantfork.stats"(%arg0) {layerStats = dense<[-1.0, 1.0]> : tensor<2xf32>} : (tensor<1x2x3xf32>) -> tensor<1x2x3xf32>

  %1 = "tfl.pseudo_const"() {value = dense<0> : tensor<3xi32>} : () -> tensor<3xi32>
  %2 = "tfl.pseudo_const"() {value = dense<[0, 2, 0]> : tensor<3xi32>} : () -> tensor<3xi32>
  %3 = "tfl.pseudo_const"() {value = dense<1> : tensor<3xi32>} : () -> tensor<3xi32>

// NoSideEffect: %[[q_w:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
// NoSideEffect: %[[custom:.*]] = "tfl.custom"(%arg0, %[[q_w:.*]]) <{custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">}
// NoSideEffect: return %[[custom:.*]]

    %cst_7 = "tfl.pseudo_qconst"() {qtype = tensor<20x!quant.uniform<i8:f32, 0.006:-34>>, value = dense<1> : tensor<20xi8>} : () -> tensor<20x!quant.uniform<i8:f32, 0.006:-34>>
    %cst_11 = "tfl.pseudo_qconst"() {qtype = tensor<20x28x!quant.uniform<i8:f32, 0.006:-34>>, value = dense<1> : tensor<20x28xi8>} : () -> tensor<20x28x!quant.uniform<i8:f32, 0.006:-34>>

  func.return %mm : tensor<1x3x3x12xf32>

// CHECK: %[[w:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<512x12x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>,
// CHECK: %[[mm:.*]] = "tfl.batch_matmul"(%arg0, %[[w]]) <{adj_x = false, adj_y = false
// CHECK-SAME: , asymmetric_quantize_inputs = true
// CHECK: return %[[mm:.*]]

// PerTensor: %[[w:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<512x12x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>,

  %1 = "tfl.pseudo_qconst"() {qtype = tensor<32x3x3x3x!quant.uniform<i8<-127:127>:f32, 0.021826678373682216:151>>, value = dense<-76> : tensor<32x3x3x3xi8>} : () -> tensor<32x3x3x3x!quant.uniform<i8<-127:127>:f32, 0.021826678373682216>>

# MLIR-SAME:  outputs = "output"
# MLIR:         %[[shape:.*]] = arith.constant dense<[1, -1, 31]> : tensor<3xi32>
# MLIR:         %[[bias:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x!quant.uniform<i32:f32:0
# MLIR:         %[[weight:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x1x1x256x!quant.uniform<i8<-127:127>:f32:0, {0.12581039038230116,