code: def conv2d_strategy_cpu(attrs, inputs, out_type, target):
      """conv2d x86 strategy"""
      strategy = _op.OpStrategy()
      data, kernel = inputs
      stride_h, stride_w = get_const_tuple(attrs.strides)
      dilation_h, dilation_w = get_const_tuple(attrs.dilation)
      groups = attrs.groups
      layout = attrs.data_layout
      kernel_layout = attrs.kernel_layout
      if dilation_h < 1 or dilation_w < 1:
          raise ValueError("dilation should be positive value")
  
      need_auto_scheduler_layout = is_auto_scheduler_enabled()
      need_meta_schedule_layout = is_meta_schedule_enabled()
  
      if groups == 1:
          if layout == "NCHW":
              assert kernel_layout == "OIHW"
              if topi.x86.is_int8_hw_support(data.dtype, kernel.dtype):
                  strategy.add_implementation(
                      wrap_compute_conv2d(topi.x86.conv2d_nchw_int8),
                      wrap_topi_schedule(
                          topi.x86.schedule_conv2d_nchw_int8
                      ),
                      name="conv2d_nchw_int8.x86",
                  )
              elif "dnnl" in target.libs:
                  strategy.add_implementation(
                      wrap_compute_conv2d(topi.x86.conv2d_nchw_dnnl),
                      wrap_topi_schedule(
                          topi.x86.schedule_conv2d_nchw_dnnl
                      ),
                      name="conv2d_nchw_dnnl.x86",
                  )
              else:
                  strategy.add_implementation(
                      wrap_compute_conv2d(topi.x86.conv2d_nchw),
                      wrap_topi_schedule(topi.x86.schedule_conv2d_nchw),
                      name="conv2d_nchw.x86",
                  )
          elif _NCHWc_matcher.match(
              layout
          ):  # check if layout is NCHWxc
              assert _OIHWio_matcher.match(
                  kernel_layout
              )  # check if kernel is OIHWio
              return conv2d_NCHWc_strategy_cpu(
                  attrs, inputs, out_type, target
              )
          elif layout == "NHWC":
              assert kernel_layout == "HWIO"
              if (not need_auto_scheduler_layout) and (
                  not need_meta_schedule_layout
              ):
                  logger.warning(
                      "conv2d NHWC layout is not optimized for x86 with autotvm."
                  )
              if "dnnl" in target.libs:
                  strategy.add_implementation(
                      wrap_compute_conv2d(topi.x86.conv2d_nhwc_dnnl),
                      wrap_topi_schedule(
                          topi.x86.schedule_conv2d_nhwc_dnnl
                      ),
                      name="conv2d_nhwc_dnnl.x86",
                  )
              else:
                  strategy.add_implementation(
                      wrap_compute_conv2d(
                          topi.nn.conv2d_nhwc,
                          need_auto_scheduler_layout=need_auto_scheduler_layout,
                          need_meta_schedule_layout=need_meta_schedule_layout,
                      ),
                      wrap_topi_schedule(topi.x86.schedule_conv2d_nhwc),
                      name="conv2d_nhwc.x86",
                  )
  
              judge_winograd_auto_scheduler = False
              if len(kernel.shape) == 4:
                  kernel_h, kernel_w, _, co = get_const_tuple(
                      kernel.shape
                  )
                  judge_winograd_auto_scheduler = (
                      "float" in data.dtype
                      and "float" in kernel.dtype
                      and kernel_h == 3
                      and kernel_w == 3
                      and stride_h == 1
                      and stride_w == 1
                      and dilation_h == 1
                      and dilation_w == 1
                      and 64 < co < 512
                      # The last condition of co is based on our profiling of resnet workloads
                      # on skylake avx512 machines. We found winograd is faster than direct
                      # only when co is within this range
                  )
  
              # register auto-scheduler implementations
              if (
                  need_auto_scheduler_layout
                  or need_meta_schedule_layout
              ) and judge_winograd_auto_scheduler:
                  strategy.add_implementation(
                      wrap_compute_conv2d(
                          topi.nn.conv2d_winograd_nhwc,
                          need_auto_scheduler_layout=need_auto_scheduler_layout,
                          need_meta_schedule_layout=need_meta_schedule_layout,
                      ),
                      naive_schedule,  # this implementation should never be picked by autotvm
                      name="conv2d_nhwc.winograd",
                      plevel=15,
                  )
          elif layout == "HWCN":
              assert kernel_layout == "HWIO"
              if (not need_auto_scheduler_layout) or (
                  not need_meta_schedule_layout
              ):
                  logger.warning(
                      "conv2d HWCN layout is not optimized for x86 with autotvm."
                  )
              strategy.add_implementation(
                  wrap_compute_conv2d(topi.nn.conv2d_hwcn),
                  wrap_topi_schedule(topi.generic.schedule_conv2d_hwcn),
                  name="conv2d_hwcn.generic",
              )
          else:
              raise RuntimeError(
                  f"Unsupported conv2d layout {layout} for x86"
              )
      elif is_depthwise_conv2d(
          data.shape, layout, kernel.shape, kernel_layout, groups
      ):
          if layout == "NCHW":
              assert kernel_layout == "OIHW"
              channel_multiplier = get_const_tuple(inputs[1].shape)[1]
              if (
                  channel_multiplier == 1
                  and dilation_h == 1
                  and dilation_w == 1
              ):
                  strategy.add_implementation(
                      wrap_compute_conv2d(
                          topi.x86.depthwise_conv2d_nchw
                      ),
                      wrap_topi_schedule(
                          topi.x86.schedule_depthwise_conv2d_nchw
                      ),
                      name="depthwise_conv2d_nchw.x86",
                  )
              else:
                  logger.warning(
                      "For x86 target, depthwise_conv2d with channel "
                      "multiplier greater than 1 is not optimized"
                  )
                  strategy.add_implementation(
                      wrap_compute_conv2d(
                          topi.nn.depthwise_conv2d_nchw
                      ),
                      wrap_topi_schedule(
                          topi.generic.schedule_depthwise_conv2d_nchw
                      ),
                      name="depthwise_conv2d_nchw.generic",
                  )
          elif _NCHWc_matcher.match(
              layout
          ):  # check if layout is NCHWxc
              assert _OIHWio_matcher.match(
                  kernel_layout
              )  # check if kernel is OIHWio
              return depthwise_conv2d_NCHWc_strategy_cpu(
                  attrs, inputs, out_type, target
              )
          elif layout == "NHWC":
              if (not need_auto_scheduler_layout) and (
                  not need_meta_schedule_layout
              ):
                  logger.warning(
                      "depthwise_conv2d NHWC layout is not optimized for x86 with autotvm."
                  )
              strategy.add_implementation(
                  wrap_compute_conv2d(
                      topi.nn.depthwise_conv2d_nhwc,
                      need_kernel_layout=True,
                  ),
                  wrap_topi_schedule(
                      topi.generic.schedule_depthwise_conv2d_nhwc
                  ),
                  name="depthwise_conv2d_nhwc.generic",
              )
          else:
              raise RuntimeError(
                  f"Unsupported depthwise_conv2d layout {layout}"
              )
      else:  # group_conv2d
          if layout == "NCHW":
              assert kernel_layout == "OIHW"
              strategy.add_implementation(
                  wrap_compute_conv2d(
                      topi.x86.group_conv2d_nchw, has_groups=True
                  ),
                  wrap_topi_schedule(
                      topi.x86.schedule_group_conv2d_nchw
                  ),
                  name="group_conv2d_nchw.x86",
              )
          elif layout == "NHWC":
              assert kernel_layout == "HWIO"
              if (not need_auto_scheduler_layout) and (
                  not need_meta_schedule_layout
              ):
                  logger.warning(
                      "group_conv2d is not optimized for x86 with autotvm."
                  )
              strategy.add_implementation(
                  wrap_compute_conv2d(
                      topi.nn.group_conv2d_nhwc, has_groups=True
                  ),
                  wrap_topi_schedule(
                      topi.generic.schedule_group_conv2d_nhwc
                  ),
                  name="group_conv2d_nhwc.generic",
              )
          elif _NCHWc_matcher.match(
              layout
          ):  # check if layout is NCHWxc
              assert _OIHWio_matcher.match(
                  kernel_layout
              )  # check if kernel is OIHWio
              return conv2d_NCHWc_strategy_cpu(
                  attrs, inputs, out_type, target
              )
          else:
              raise RuntimeError(
                  f"Unsupported group_conv2d layout {layout}"
              )
      return strategy
  
imports: import logging
  import re
  from tvm import tir, topi
  from tvm.auto_scheduler import is_auto_scheduler_enabled
  from tvm.meta_schedule import is_meta_schedule_enabled
  from tvm.relay.ty import is_dynamic
  from tvm.te import SpecializedCondition
  from ..None import op
  from .generic import *
label_desc: Use image node_id 8 for calling a global function with example usage: conv2d_strategy_cpu(attrs, inputs, out_type, target) and returns: strategy, conv2d_NCHWc_strategy_cpu, depthwise_conv2d_NCHWc_strategy_cpu, conv2d_NCHWc_strategy_cpu