// CHECK-SAME: <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 2, 2, 1]}>
// Check that the `attr_map` attribute has been removed.
// CHECK-NOT: attr_map

// -----

func.func @conv_with_non_constant_filter(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<*xf32> {

    func.return %1: tensor<*xf32>
  }
  func.func private @composite_matmul_fn(%arg0: tensor<1x2x2x3xf32>, %arg1: tensor<2x1024xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {
    %0 = "tf.MatMul"(%arg0, %arg1) {attr_map = "0:transpose_a,1:transpose_a", device = "", transpose_a = false, transpose_b = false} : (tensor<1x2x2x3xf32>, tensor<2x1024xf32>) -> tensor<*xf32>
    return %0 : tensor<*xf32>
  }

// CHECK-LABEL: func @matmul

    func.return %1: tensor<*xf32>
  }
  func.func private @composite_matmul_fn(%arg0: tensor<1x2x2x3xf32>, %arg1: tensor<2x1024xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {
    %0 = "tf.MatMul"(%arg0, %arg1) {attr_map = "0:transpose_a,1:transpose_a", device = "", transpose_a = false, transpose_b = false} : (tensor<1x2x2x3xf32>, tensor<2x1024xf32>) -> tensor<*xf32>
    return %0 : tensor<*xf32>
  }

// CHECK-LABEL: func @matmul

    %cst_0 = "tf.Const"() {value = dense<2.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32>
    %0 = "tf.MatMul"(%arg0, %cst_0) {attr_map = "0:transpose_a,1:transpose_a", device = "", transpose_a = false, transpose_b = false} : (tensor<1x2x2x2xf32>, tensor<2x1024xf32>) -> tensor<*xf32>

        output_quantization_axis = -1,
        output_quantization_min_val = -2147483648,
        output_quantization_max_val = 2147483647,
        T = "tfdtype$DT_QINT32",
        attr_map = ""
      } : (tensor<*x!tf_type.qint32>, tensor<*x!tf_type.qint32>, tensor<*xf32>, tensor<*xi32>, tensor<*xf32>, tensor<*xi32>, tensor<*xf32>, tensor<*xi32>) -> tensor<*x!tf_type.qint32>

  for (const auto& name_attr_pair : n.attrs()) {
    const AttrValue& attr_value = name_attr_pair.second;
    if (attr_value.value_case() == AttrValue::kFunc) {
      result.push_back(attr_value.func());
    } else if (attr_value.value_case() == AttrValue::kList) {
      result.insert(result.end(), attr_value.list().func().begin(),
                    attr_value.list().func().end());
    }
  }

  return result;
}

    const std::pair<string, bool>& bool_attr) {
  AttrValue attr_value;
  attr_value.set_b(bool_attr.second);
  return {bool_attr.first, attr_value};
}

std::pair<string, AttrValue> impl::AttrLiteralHelper(
    const std::pair<string, absl::Span<const int>>& int_list_attr) {
  AttrValue attr_value;
  AttrValue::ListValue* list = attr_value.mutable_list();
  for (int i : int_list_attr.second) {
    list->add_i(i);

  }
  func.func private @composite_depthwise_conv2d_fn(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {
    %0 = "tf.DepthwiseConv2dNative"(%arg0, %arg1) {
      attr_map = "0:strides,1:padding,2:explicit_paddings,3:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1]

// CHECK-NEXT: %[[matmul:.*]] = "tf.MatMul"(%arg0, %arg1)
// CHECK-SAME: attr_map = "0:transpose_a,1:transpose_b"
// CHECK-NEXT: tf.BiasAdd
// CHECK-NEXT: tf.Relu6
// CHECK-NEXT: return

// CHECK-LABEL: private @composite_matmul_with_bias_and_relu_fn_1
// CHECK-NEXT: tf.MatMul"(%arg0, %arg1)
// CHECK-SAME: attr_map = "0:transpose_a,1:transpose_b"
// CHECK-NEXT: tf.BiasAdd
// CHECK-NEXT: tf.Relu

    func.return %1: tensor<*xf32>
  }
  func.func private @composite_matmul_fn_1(%arg0: tensor<2x12xf32>, %arg1: tensor<12x2xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {
    %0 = "tf.MatMul"(%arg0, %arg1) {attr_map = "0:transpose_a,1:transpose_b", device = "", transpose_a = false, transpose_b = false} : (tensor<2x12xf32>, tensor<12x2xf32>) -> tensor<*xf32>
    return %0 : tensor<*xf32>
  }

// CHECK-LABEL: func @matmul