code: def _convert_separable_convolution(
      inexpr, keras_layer, etab, data_layout, input_shape=None
  ):
      _check_data_format(keras_layer)
  
      if data_layout == "NHWC":
          kernel_layout = "HWOI"
      else:
          kernel_layout = "OIHW"
  
      if input_shape is None:
          input_shape = keras_layer.input_shape
  
      weightList = keras_layer.get_weights()
      # depthwise conv
      kernel_h, kernel_w, in_channels, depth_mult = weightList[0].shape
      stride_h, stride_w = keras_layer.strides
      if kernel_layout == "OIHW":
          weight0 = weightList[0].transpose([2, 3, 0, 1])
      else:
          weight0 = weightList[0]
      if isinstance(keras_layer.dilation_rate, (list, tuple)):
          dilation = [
              keras_layer.dilation_rate[0],
              keras_layer.dilation_rate[1],
          ]
      else:
          dilation = [
              keras_layer.dilation_rate,
              keras_layer.dilation_rate,
          ]
      params0 = {
          "weight": etab.new_const(weight0),
          "channels": in_channels * depth_mult,
          "groups": in_channels,
          "kernel_size": [kernel_h, kernel_w],
          "strides": [stride_h, stride_w],
          "dilation": dilation,
          "padding": [0, 0],
          "data_layout": data_layout,
          "kernel_layout": kernel_layout,
      }
      if keras_layer.padding == "valid":
          pass
      # we insert a separate pad operator
      elif keras_layer.padding == "same":
          in_h = input_shape[1]
          in_w = input_shape[2]
          pad_t, pad_b = _get_pad_pair(in_h, kernel_h, stride_h)
          pad_l, pad_r = _get_pad_pair(in_w, kernel_w, stride_w)
          params0["padding"] = (pad_t, pad_l, pad_b, pad_r)
      else:
          msg = (
              f"Padding with {keras_layer.padding} is not supported for operator Separable "
              f"Convolution in frontend Keras."
          )
          raise tvm.error.OpAttributeUnImplemented(msg)
      depthconv = _op.nn.conv2d(data=inexpr, **params0)
      # pointwise conv
      if kernel_layout == "OIHW":
          weight1 = weightList[1].transpose([3, 2, 0, 1])
      else:
          weight1 = weightList[1]
          kernel_layout = "HWIO"
      params1 = {
          "weight": etab.new_const(weight1),
          "channels": weightList[1].shape[3],
          "groups": 1,
          "kernel_size": [1, 1],
          "strides": [1, 1],
          "dilation": [1, 1],
          "data_layout": data_layout,
          "kernel_layout": kernel_layout,
      }
      out = _op.nn.conv2d(data=depthconv, **params1)
      if keras_layer.use_bias:
          bias = etab.new_const(weightList[2])
          if data_layout == "NCHW":
              out = _op.nn.bias_add(out, bias)
          else:
              out = _op.nn.bias_add(out, bias, axis=-1)
      # defuse activation
      if sys.version_info.major < 3:
          act_type = keras_layer.activation.func_name
      else:
          act_type = keras_layer.activation.__name__
      if act_type != "linear":
          out = _convert_activation(out, act_type, etab, data_layout)
      return out
  
imports: import dis
  import sys
  import numpy
  import tvm
  from tvm.ir import IRModule, TensorType, TupleType
  from ..None import analysis
  from ..None import expr
  from ..None import function
  from ..None import op
  from ...None import nd
  from .common import ExprTable, new_var
label_desc: Use image node_id 15 for calling a global function with example usage: _convert_separable_convolution(inexpr, keras_layer, etab, data_layout, input_shape) and returns: out