// CHECK: "tfl.conv_2d"(%arg0, %arg1, %arg2) <{dilation_h_factor = 2 : i32, dilation_w_factor = 2 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<1x112x80x128xf32>, tensor<128x3x3x128xf32>, tensor<128xf32>) -> tensor<1x108x76x128xf32>

  // CHECK-DAG: %[[VAL0:.+]] = "tfl.pseudo_const"() <{value = dense<42> : tensor<16x1x1x8xi8>}>
  // CHECK-DAG: %[[VAL1:.+]] = "tfl.pseudo_const"() <{value = dense<1> : tensor<16x1x1x8xi8>}>
  // CHECK-DAG: %[[VAL2:.+]] = "tfl.conv_2d"(%arg0, %[[VAL0]], %[[VAL1]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}>
  // CHECK: return %[[VAL2]]

  %b = arith.constant dense<0.0> : tensor<16xf32>
  %conv_3d = "tfl.conv_3d"(%arg0, %w, %b) {dilation_d_factor = 1 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_d = 1 : i32, stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x32x32x32x8xf32>, tensor<1x1x1x8x16xf32>, tensor<16xf32>) -> tensor<1x32x32x32x16xf32>
  func.return %conv_3d : tensor<1x32x32x32x16xf32>

// RUN: json_to_flatbuffer %p/test_schema.fbs %s | flatbuffer_translate --tflite-flatbuffer-to-mlir -o - | FileCheck %s

// CHECK: %[[CST:.*]] = "tfl.no_value"() <{value}> : () -> none
// CHECK: %[[RES0:.*]] = "tfl.conv_2d"(%arg0, %arg1, %[[CST]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, none) -> tensor<256x32x32x16xf32>

// This test is to test that if the flatbuffer omits the last optional input `bias` of tfl.conv_2d op, the flatbuffer_importer will automatically adds `none` value to tfl.conv_2d.

// CHECK: %[[CST:.*]] = "tfl.no_value"() <{value}> : () -> none

  %conv = "tfl.conv_2d"(%0, %w, %b) {
    dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU",
    padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32
  } : (tensor<1x5x5x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<1x5x5x3xf32>
  %conv2 = "tfl.conv_2d"(%0, %w, %b2) {

  %cst = arith.constant dense<1.5> : tensor<f16>
  %cst_0 = arith.constant dense<[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0]> : tensor<16xf16>

  %2 = "tfl.pseudo_qconst"() {qtype = tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>, value = dense<0> : tensor<32xi32>} : () -> tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>

// CHECK: %[[conv3d:.*]] = "tfl.conv_3d"(%arg0, %[[w]], %[[const]]) <{dilation_d_factor = 1 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_d = 1 : i32, stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<?x28x28x28x8xf32>, tensor<3x3x3x8x16xf32>, none) -> tensor<?x26x26x26x16xf32>
// CHECK: %2 = "tfl.shape"(%[[conv3d]]) : (tensor<?x26x26x26x16xf32>) -> tensor<5xi64>

    %1 = "tfl.dequantize"(%arg1) : (tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 1.0>>) -> tensor<32x3x3x3xf32>
    %2 = "tfl.dequantize"(%arg2) : (tensor<32x!quant.uniform<i32:f32, 1.0>>) -> tensor<32xf32>