code: def _pool2d_reshape(
      input,
      kernel_size,
      padding=None,
      stride=None,
      dilation=1,
      ceil_mode=False,
      pad_value=0,
  ):
      """Rearrange a 4-d tensor so that each kernel is represented by each row"""
  
      # Setup kernel / stride / dilation values
      k = kernel_size
      if isinstance(k, int):
          k = (k, k)
  
      s = stride
      if s is None:
          s = k
      elif isinstance(s, int):
          s = (s, s)
  
      d = dilation
      if isinstance(d, int):
          d = (d, d)
  
      # Assert input parameters are correct type / size
      assert isinstance(k, tuple), "kernel_size must be an int or tuple"
      assert isinstance(
          s, tuple
      ), "stride must be and int, a tuple, or None"
      assert len(k) == 2, "kernel_size must be an int or tuple pair"
      assert len(s) == 2, "stride must be an int or tuple pair"
      assert isinstance(pad_value, int), "pad_value must be an integer"
      assert (
          input.dim() >= 2
      ), "Pooling input dimension should be at least 2"
  
      # Apply padding if necessary
      if padding is not None:
          padding = (
              (padding, padding)
              if isinstance(padding, int)
              else padding
          )
          assert (
              len(padding) == 2
          ), "Padding must be an integer or a pair"
          padding = (padding[0], padding[0], padding[1], padding[1])
      else:
          padding = (0, 0, 0, 0)
  
      # Compute output size based on parameters
      n = input.size()[:-2]
      h = _pooling_output_shape(
          input.size(-2),
          k[0],
          padding[0],
          padding[1],
          s[0],
          d[0],
          ceil_mode,
      )
      w = _pooling_output_shape(
          input.size(-1),
          k[1],
          padding[2],
          padding[3],
          s[1],
          d[1],
          ceil_mode,
      )
  
      out_size = tuple(n + (h, w))
  
      input = torch.nn.functional.pad(input, padding, value=pad_value)
      if ceil_mode:
          update_pad = [0, 0, 0, 0]
          update_pad[3] = h * s[0] + (k[0] - 1) * d[0] - input.size(-2)
          update_pad[1] = w * s[1] + (k[1] - 1) * d[1] - input.size(-1)
          input = torch.nn.functional.pad(
              input, tuple(update_pad), value=pad_value
          )
  
      # Reshape input to arrange kernels to be represented by rows
      kernel_indices = torch.tensor(
          range(0, k[1] * d[1], d[1]), device=input.device
      )
      kernel_indices = torch.cat(
          [
              kernel_indices + i * input.size(-1)
              for i in range(0, k[0] * d[0], d[0])
          ]
      )
      kernel_indices = torch.stack(
          [kernel_indices + i * s[1] for i in range(w)]
      )
  
      offset = input.size(-1)
      kernel_indices = torch.cat(
          [kernel_indices + i * s[0] * offset for i in range(h)]
      )
  
      for dim in range(2, input.dim()):
          offset *= input.size(-dim)
          kernel_indices = torch.stack(
              [
                  kernel_indices + i * offset
                  for i in range(input.size(-dim - 1))
              ]
          )
  
      output = input.take(kernel_indices)
      return output, out_size
  
file_path: CrypTen/crypten/common/functions/pooling.py
imports: import math
  import torch
label_desc: Use image node_id 7 for calling a global function with example usage: _pool2d_reshape(input, kernel_size, padding, stride, dilation, ceil_mode, pad_value) and returns: output,  out_size