code: def _dilate(operand, factors, fill_value=0):
      # this logic is like lax.pad, but with two leading dimensions, no edge
      # padding, and factors are at least 1 (interior padding is at least 0)
      outspace = np.add(
          operand.shape[2:],
          np.multiply(
              np.subtract(factors, 1), np.subtract(operand.shape[2:], 1)
          ),
      )
      out = np.full(
          operand.shape[:2] + tuple(outspace), fill_value, operand.dtype
      )
      lhs_slices = tuple(_slice(None, None, step) for step in factors)
      out[(_slice(None),) * 2 + lhs_slices] = operand
      return out
  
imports: import builtins
  import collections
  import itertools
  import numpy
  import opt_einsum
  import scipy.special
  from jax._src import dtypes
  from jax._src import util
label_desc: Use image node_id 40 for calling a global function with example usage: _dilate(operand, factors, fill_value) and returns: out