code: def _convert_convolution1d(
      inexpr, keras_layer, etab, data_layout, input_shape=None
  ):
      is_deconv = type(keras_layer).__name__ == "Conv1DTranspose"
  
      if input_shape is None:
          input_shape = keras_layer.input_shape
      _check_data_format(keras_layer)
      weightList = keras_layer.get_weights()
      weight = weightList[0]
  
      if data_layout == "NWC":
          kernel_layout = "WIO"
          if is_deconv:
              kernel_layout = "WOI"
      else:
          kernel_layout = "OIW"
          if is_deconv:
              kernel_layout = "IOW"
          msg = (
              f"Kernel layout with {kernel_layout} is not supported for operator Convolution1D "
              f"in frontend Keras."
          )
          raise tvm.error.OpAttributeUnImplemented(msg)
  
      if is_deconv:
          if kernel_layout == "IOW":
              weight = weight.transpose([2, 1, 0])
          kernel_w, n_filters, _ = weight.shape
      else:
          kernel_w, _, n_filters = weight.shape
  
      dilation_rate = keras_layer.dilation_rate
      if isinstance(dilation_rate, (list, tuple)):
          dilation = [dilation_rate[0]]
      else:
          dilation = [dilation_rate]
  
      dilated_kernel_w = (kernel_w - 1) * dilation[0] + 1
      stride_w = keras_layer.strides[0]
      params = {
          "weight": etab.new_const(weight),
          "kernel_size": [kernel_w],
          "strides": [stride_w],
          "dilation": dilation,
          "padding": [0],
          "data_layout": data_layout,
          "kernel_layout": kernel_layout,
      }
      params["channels"] = n_filters
  
      if keras_layer.padding == "valid":
          pass
      # calculate the padding values
      elif keras_layer.padding == "same":
          in_w = input_shape[1]
          pad_w = _get_pad_pair(in_w, dilated_kernel_w, stride_w)
          params["padding"] = [pad_w[0], pad_w[1]]
      else:
          msg = (
              f"Padding with {keras_layer.padding} is not supported for operator Convolution3D "
              f"in frontend Keras."
          )
          raise tvm.error.OpAttributeUnImplemented(msg)
  
      if is_deconv:
          out = _op.nn.conv1d_transpose(data=inexpr, **params)
      else:
          out = _op.nn.conv1d(data=inexpr, **params)
  
      channel_axis = -1 if data_layout == "NWC" else 1
      if keras_layer.use_bias:
          bias = etab.new_const(weightList[1])
          out = _op.nn.bias_add(out, bias, channel_axis)
  
      # 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 12 for calling a global function with example usage: _convert_convolution1d(inexpr, keras_layer, etab, data_layout, input_shape) and returns: out