code: def transposed_convNd(
      input, kernel, crop, stride=1, n=None, extend=None
  ):
      if n is None:
          n = input.ndim - 2
      if crop == "valid":
          pad = "full"
      elif crop == "full":
          pad = "valid"
      elif crop == "same":
          pad = "same"
      else:
          crop = as_tuple(crop, n, int)
          pad = tuple(f - 1 - c for f, c in zip(kernel.shape[2:], crop))
      stride = as_tuple(stride, n, int)
      dilated_input = dilate(input, (1, 1) + stride)
      if extend is not None:
          extend = as_tuple(extend, n, int)
          extend = [(0, p) for p in (0, 0) + extend]
          dilated_input = np.pad(dilated_input, extend, mode="constant")
      return convNd(dilated_input, kernel, pad, stride=1, n=n)
  
imports: import numpy
  import pytest
  import importlib
  import theano
  from theano import tensor
  import lasagne
  from lasagne.utils import floatX, as_tuple
label_desc: Use image node_id 3 for calling a global function with example usage: transposed_convNd(input, kernel, crop, stride, n, extend) and returns: convNd