// 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

  %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>

    %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>

    }
    func.func private @composite_fn_1(%arg0: tensor<1x4x4x3xf32>, %arg1: tensor<3x1x1x3xf32>, %arg2: tensor<3xf32>) -> tensor<1x4x4x3xf32> attributes {tf_quant.composite_function} {

  %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>>

      if (function_name.contains("with_bias")) {
        spec->biases_params[2] = {{0, 1},
                                  quant::GetUniformQuantizedTypeForBias};
      }
    } else if (function_name.contains("conv2d")) {
      spec->coeff_op_quant_dim[1] = 3;
      if (function_name.contains("with_bias")) {
        spec->biases_params[2] = {{0, 1},
                                  quant::GetUniformQuantizedTypeForBias};
      }

  Output const_0d = ops::Const(root.WithOpName("const_0d"), 42);

  Output const_2d = ops::Const(root.WithOpName("const_2d"), {{1, 2}, {4, 3}});

  EXPECT_THAT(const_0d.node(), NodeWith(ConstantValue(42)));
  EXPECT_THAT(const_0d.node(), NodeWith(ConstantValue(42), Name("const_0d")));

  EXPECT_THAT(const_2d.node(), NodeWith(ConstantValue({{1, 2}, {4, 3}})));

      if (!input_type || !input_type.hasStaticShape()) {
        return false;
      }
      total_count += input_type.getNumElements();
    }
    *count = total_count;
    return true;
  }

  // For conv2d/depthwise_conv/fully_connected ops.
  // This algorithm actually comes from TOCO tooling_util.cc
  static bool GetArithmeticCountForConvAndFullyconnectedOp(mlir::Operation* op,

  %0 = "tf.Conv2D"(%output, %cst) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 2, 2, 1], use_cudnn_on_gpu = true}> {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", device...