`attr_name` is given, check if the `attr_val` matches with the attribute
      value of the op.
    """
    # Check the main graph
    if self._contains_op_with_name_and_attribute(
        nodes=graphdef.node,
        op_name=op_name,
        attr_name=attr_name,
        attr_val=attr_val,
        node_name=node_name,
    ):
      return True

      if attr_val.type == types_pb2.DT_FLOAT:
        return 'f32'
      elif attr_val.type == types_pb2.DT_INT32:
        return 'i32'
      elif attr_val.type == types_pb2.DT_INT64:
        return 'i64'
      elif attr_val.type == types_pb2.DT_BOOL:
        return 'i1'
    # list
    if attr_val.HasField('list'):
      if attr_val.list.f:
        elt_ty = TFRTypes.F32
        values = attr_val.list.f

    %0 = "tf.MatMul"(%arg0, %arg1) {attr_map = "0:transpose_a,1:transpose_b", device = "", transpose_a = false, transpose_b = false} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
    return %0 : tensor<2x2xf32>
  }
  func.func private @composite_matmul_fn_2_0(%arg0: tensor<2x2xf32>, %arg1: tensor<2x2xf32>) -> tensor<2x2xf32> attributes {tf_quant.composite_function} {

        float_func.emitError("The attr_map attribute is malformed");
        return failure();
      }
      identifier_to_attr.insert(
          {llvm::StringRef(std::string(key_and_value_pair[1])),
           inner_op.getAttr(
               llvm::StringRef(std::string(key_and_value_pair[1])))});
    }
  }

  // Set the attributes for ops with the attr_map attribute.

  EXPECT_EQ(0, TF_OperationNumControlOutputs(add));

  ASSERT_TRUE(GetAttrValue(add, "T", &attr_value, s)) << TF_Message(s);
  EXPECT_EQ(attr_value.type(), tensorflow::DT_INT32);
  ASSERT_TRUE(GetAttrValue(add, "N", &attr_value, s)) << TF_Message(s);
  EXPECT_EQ(attr_value.i(), 2);

  // Placeholder oper now has a consumer.
  ASSERT_EQ(1, TF_OperationOutputNumConsumers(TF_Output{feed, 0}));

                              const char* attr_name) {
  bool out = false;
  bool attr_value;
  if (TryGetNodeAttr(node->attrs(), attr_name, &attr_value)) {
    out |= attr_value;
  }

  if (flib_def->GetAttr(*node, attr_name, &attr_value).ok()) {
    out |= attr_value;
  }
  return out;
}

Status MarkForCompilationPassImpl::BuildInitialClusterSet() {

                        const char* attr_name, const char* attr_value,
                        TF_Operation** op) {
  TF_OperationDescription* desc = TF_NewOperation(graph, "Placeholder", name);
  TF_SetAttrType(desc, "dtype", TF_INT32);
  TF_SetAttrString(desc, attr_name, attr_value, strlen(attr_value));
  *op = TF_FinishOperation(desc, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

#include "tensorflow/c/tf_tensor.h"
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/common_runtime/device_factory.h"
#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/device_base.h"
#include "tensorflow/core/framework/kernel_def.pb.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/node_def_builder.h"

#include "tensorflow/c/eager/c_api.h"

#include <string.h>

#include <memory>
#include <string>

// clang-format off
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/platform.h"
// clang-format on

#include "absl/strings/match.h"
#include "tensorflow/c/eager/c_api_experimental.h"