code: def conv1d_sc(
      input,
      filters,
      image_shape=None,
      filter_shape=None,
      border_mode="valid",
      subsample=(1,),
      filter_flip=True,
  ):
      """
      using conv2d with a single input channel
      """
      if border_mode not in ("valid", 0, (0,)):
          raise RuntimeError(
              "Unsupported border_mode for conv1d_sc: "
              "%s" % border_mode
          )
  
      if image_shape is None:
          image_shape_sc = None
      else:
          # (b, c, i0) to (b, 1, c, i0)
          image_shape_sc = (
              image_shape[0],
              1,
              image_shape[1],
              image_shape[2],
          )
  
      if filter_shape is None:
          filter_shape_sc = None
      else:
          filter_shape_sc = (
              filter_shape[0],
              1,
              filter_shape[1],
              filter_shape[2],
          )
  
      input_sc = input.dimshuffle(0, "x", 1, 2)
      # We need to flip the channels dimension because it will be convolved over.
      filters_sc = filters.dimshuffle(0, "x", 1, 2)[:, :, ::-1, :]
  
      conved = T.nnet.conv2d(
          input_sc,
          filters_sc,
          image_shape_sc,
          filter_shape_sc,
          subsample=(1, subsample[0]),
          filter_flip=filter_flip,
      )
      return conved[:, :, 0, :]
  
file_path: Lasagne/lasagne/theano_extensions/conv.py
imports: import numpy
  import theano.tensor
  from ..utils import int_types
label_desc: Use image node_id 1 for calling a global function with example usage: conv1d_sc(input, filters, image_shape, filter_shape, border_mode, subsample, filter_flip) and returns: conved