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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def multi_interpolation_basis(n_objectives=6, n_interp_steps=5, width=128, channels=3):
 """A paramaterization for interpolating between each pair of N objectives. Sometimes you want to interpolate between optimizing a bunch of objectives, in a paramaterization that encourages images to align. Args: n_objectives: number of objectives you want interpolate between n_interp_steps: number of interpolation steps width: width of intepolated images channel Returns: A [n_objectives, n_objectives, n_interp_steps, width, width, channel] shaped tensor, t, where the final [width, width, channel] should be seen as images, such that the following properties hold: t[a, b] = t[b, a, ::-1] t[a, i, 0] = t[a, j, 0] for all i, j t[a, a, i] = t[a, a, j] for all i, j t[a, b, i] = t[b, a, -i] for all i """

  N, M, W, Ch = n_objectives, n_interp_steps, width, channels

  const_term = sum([lowres_tensor([W, W, Ch], [W//k, W//k, Ch])
                    for k in [1, 2, 4, 8]])
  const_term = tf.reshape(const_term, [1, 1, 1, W, W, Ch])

  example_interps = [
      sum([lowres_tensor([M, W, W, Ch], [2, W//k, W//k, Ch])
           for k in [1, 2, 4, 8]])
      for _ in range(N)]

  example_basis = []
  for n in range(N):
    col = []
    for m in range(N):
      interp = example_interps[n] + example_interps[m][::-1]
      col.append(interp)
    example_basis.append(col)

  interp_basis = []
  for n in range(N):
    col = [interp_basis[m][N-n][::-1] for m in range(n)]
    col.append(tf.zeros([M, W, W, 3]))
    for m in range(n+1, N):
      interp = sum([lowres_tensor([M, W, W, Ch], [M, W//k, W//k, Ch])
                    for k in [1, 2]])
      col.append(interp)
    interp_basis.append(col)

  basis = []
  for n in range(N):
    col_ex = tf.stack(example_basis[n])
    col_in = tf.stack(interp_basis[n])
    basis.append(col_ex + col_in)
  basis = tf.stack(basis)

  return basis + const_term