code: def handle_conv2d(
      cutlass_profiler,
      op_type,
      d_shape,
      w_shape,
      padding,
      strides,
      dilation,
      out_dtype,
      data_dtype,
      weight_dtype,
      use_3xtf32,
      split_k_slices,
      profile_all_alignments,
      find_first_valid,
      use_multiprocessing,
  ):
      """Profile and select a kernel for conv2d op workload."""
      if "conv2d_transpose" in op_type:
          conv_kind = ConvKind.Dgrad
      elif "backward_weight" in op_type:
          conv_kind = ConvKind.Wgrad
      else:
          conv_kind = ConvKind.Fprop
  
      if any(isinstance(s, tvm.tir.Any) for s in d_shape):
          out = cutlass_profiler.get_default(
              op_type,
              out_dtype,
              data_dtype,
              weight_dtype,
              use_3xtf32,
              conv_kind,
              strides,
          )
          name, cutlass_op_def = out["name"], out["opdef"]
          logger.info("Picked the default kernel %s", name)
      else:
          name, cutlass_op_def, _ = cutlass_profiler.profile(
              op_type,
              d_shape,
              w_shape,
              padding,
              strides,
              dilation,
              out_dtype,
              data_dtype,
              weight_dtype,
              use_3xtf32,
              conv_kind,
              split_k_slices,
              profile_all_alignments,
              find_first_valid=find_first_valid,
              use_multiprocessing=use_multiprocessing,
          )
          if not find_first_valid:
              logger.info("The best kernel is %s", name)
          else:
              logger.info("Picked the first kernel found %s", name)
  
      return {"cutlass_op_def": cutlass_op_def, "cutlass_op_name": name}
  
imports: import logging
  import os
  import multiprocessing
  import tvm
  from tvm import relay, runtime
  from tvm._ffi.registry import register_func
  from tvm.contrib.nvcc import get_cuda_version
  from .gen_conv2d import CutlassConv2DProfiler
  from .gen_gemm import CutlassGemmProfiler
  from .library import ConvKind
label_desc: Use image node_id 7 for calling a global function with example usage: handle_conv2d(cutlass_profiler, op_type, d_shape, w_shape, padding, strides, dilation, out_dtype, data_dtype, weight_dtype, use_3xtf32, split_k_slices, profile_all_alignments, find_first_valid, use_multiprocessing) and returns: dict