class_docstr: PixelShuffle layer for re-org channel to spatial dimention.
  
  Parameters
  ----------
  upscale_factor : int
      Upscaling factor for input->output spatially.
class_docstr_tok: ["PixelShuffle","layer","for","re-org","channel","to","spatial","dimention",".","Parameters","--","--","--","--","--","upscale_factor",":","int","Upscaling","factor","for","input-",">","output","spatially","."]
code: def hybrid_forward(self, F, x):
      f1, f2 = self.upscale_factor, self.upscale_factor
      # (N, f1*f2*C, H, W)
      x = F.reshape(
          x, (0, -4, -1, f1 * f2, 0, 0)
      )  # (N, C, f1*f2, H, W)
      x = F.reshape(x, (0, 0, -4, f1, f2, 0, 0))  # (N, C, f1, f2, H, W)
      x = F.transpose(x, (0, 1, 4, 2, 5, 3))  # (N, C, H, f1, W, f2)
      x = F.reshape(x, (0, 0, -3, -3))  # (N, C, H*f1, W*f2)
      return x
  
imports: import math
  import os
  import mxnet
  from mxnet import initializer
  from mxnet.gluon import nn
  from mxnet.gluon.block import HybridBlock
  from .utils import ZeroUniform, _try_load_parameters
label_desc: Use image node_id 2 for calling the PixelShuffle obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: x