code: def conv1d_transpose_ncw_python(
      a_np, w_np, stride, padding, output_padding
  ):
      """Transposed 1D convolution operator in NCW layout.
  
      Parameters
      ----------
      a_np : numpy.ndarray
          3-D with shape [batch, in_channel, in_width]
  
      w_np : numpy.ndarray
          3-D with shape [in_channel, num_filter, filter_width]
  
      stride : int or a list/tuple of one int
          Stride size, or [stride_width]
  
      padding : int, tuple, or str
          Single int for padding size, or
          tuple of 2 ints for left and right padding, or
          ['VALID', 'SAME']
  
      output_padding : tuple
          Used to recover the actual output shape in case more than one
          is possible
  
      Returns
      -------
      b_np : np.ndarray
          3-D with shape [batch, out_channel, out_width]
  
      """
      batch, in_c, in_w = a_np.shape
      _, out_c, filter_w = w_np.shape
      opad = output_padding[0]
      if isinstance(stride, int):
          stride_w = stride
      else:
          stride_w = stride[0]
      assert opad < stride_w
      fpad_left, fpad_right = get_pad_tuple1d(padding, filter_w)
      # dilate stage
      dilated_a_np = tvm.topi.testing.dilate_python(
          a_np, [1, 1, stride_w]
      )
      # padding stage
      bpad_left = filter_w - 1 - fpad_left
      bpad_right = filter_w - 1 - fpad_right + opad
      padded_a_np = np.zeros(
          (batch, in_c, dilated_a_np.shape[2] + bpad_left + bpad_right)
      )
      padded_a_np[
          :, :, bpad_left : dilated_a_np.shape[2] + bpad_left
      ] = dilated_a_np
      # convolution stage
      out_w = (
          (in_w - 1) * stride_w
          - fpad_left
          - fpad_right
          + filter_w
          + opad
      )
      b_np = np.zeros((batch, out_c, out_w))
      for n in range(batch):
          for f in range(out_c):
              for c in range(in_c):
                  out = scipy.signal.convolve(
                      padded_a_np[n, c], w_np[c, f], mode="valid"
                  )
                  b_np[n, f] += out
      return b_np
  
imports: import numpy
  import scipy
  import tvm.topi.testing
  from tvm.topi.nn.utils import get_pad_tuple1d
label_desc: Use image node_id 2 for calling a global function with example usage: conv1d_transpose_ncw_python(a_np, w_np, stride, padding, output_padding) and returns: b_np