// (spatial_filter_shape[i]-1) * dilation_rate[i]) / strides[i])
    auto get_output_dim_for_valid_padding =
        [](int64_t input_dim, int64_t dilation_dim, int64_t kernel_dim,
           int64_t stride_dim) -> int64_t {
      return std::ceil((input_dim - (kernel_dim - 1) * dilation_dim) /
                       stride_dim);
    };
    return output_height ==

        // padding[2 * i] == padding[2 * i + 1] means equal padding is applied
        // on both sides of a spatial dimension.
        // This happens when kernel_dim >= stride
        return false;
      } else if ((padding[2 * i] != padding[2 * i + 1]) &&
                 (((kernel_size - 1) != padding[2 * i]) ||
                  ((stride - 1) != padding[2 * i + 1]) ||

  # TODO(gunan): Add sanity checks to loaded modules here.

  # Load first party dynamic kernels.
  _tf_dir = _os.path.dirname(_current_file_location)
  _kernel_dir = _os.path.join(_tf_dir, "core", "kernels")
  if _os.path.exists(_kernel_dir):
    _ll.load_library(_kernel_dir)

  # Load third party dynamic kernels.
  for _s in _site_packages_dirs:
    _plugin_dir = _os.path.join(_s, "tensorflow-plugins")

  # TODO(gunan): Add sanity checks to loaded modules here.

  # Load first party dynamic kernels.
  _tf_dir = _os.path.dirname(_current_file_location)
  _kernel_dir = _os.path.join(_tf_dir, "core", "kernels")
  if _os.path.exists(_kernel_dir):
    _ll.load_library(_kernel_dir)

  # Load third party dynamic kernels.
  for _s in _site_packages_dirs:
    _plugin_dir = _os.path.join(_s, "tensorflow-plugins")

          tensorflow::DEVICE_CPU_XLA_JIT,
          /*include_compilation_only_kernels=*/true);

  std::set<std::string> xla_op_kernels;
  for (auto kernel_def : kernel_defs) {
    std::string tf_name = "tf." + kernel_def->op();
    xla_op_kernels.insert(tf_name);
  }

  DialectRegistry dialect_registry;
  mlir::RegisterCommonToolingDialects(dialect_registry);

  TF_KernelBuilder* builder = TF_NewKernelBuilder(
      op_name, device_name, &MyCreateFunc, &MyComputeFunc, &MyDeleteFunc);

  KernelDef kernel_def;
  kernel_def.set_op(op_name);
  kernel_def.set_device_type(device_name);
  std::string kernel_def_str = kernel_def.SerializePartialAsString();

  {
    TF_Status* status = TF_NewStatus();
    TF_RegisterKernelBuilderWithKernelDef(kernel_def_str.data(), node_name,

    return;
  }

  tensorflow::KernelDef* kernel_def = new tensorflow::KernelDef();
  bool success = kernel_def->ParsePartialFromString(serialized_kernel_def);
  if (!success) {
    TF_SetStatus(status, TF_INVALID_ARGUMENT,
                 "Error parsing serialized KernelDef.");
    return;
  }

  tensorflow::kernel_factory::OpKernelRegistrar(
      kernel_def, name,

#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/common_runtime/device_factory.h"
#include "tensorflow/core/common_runtime/dma_helper.h"
#include "tensorflow/core/framework/kernel_def.pb.h"
#include "tensorflow/core/framework/tensor_reference.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/public/session_options.h"
#include "tensorflow/core/tpu/tpu_defs.h"

#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/framework/device_base.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/kernel_def.pb.h"
#include "tensorflow/core/framework/node_def_builder.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor.pb.h"

#include "tensorflow/core/framework/attr_value_util.h"
#include "tensorflow/core/framework/cpp_shape_inference.pb.h"
#include "tensorflow/core/framework/full_type.pb.h"
#include "tensorflow/core/framework/kernel_def.pb.h"
#include "tensorflow/core/framework/log_memory.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/partial_tensor_shape.h"