class_docstr: Internal class that manages partitioning. The ``FakePalettize`` class base classes the ``_Partitioner`` class
  and all the partitioning logic is controlled by this class.
docstr: Method to deflatten a particular weight partition.
code: def deflatten(
      self,
      weight_partition: _torch.Tensor,
      target_size: _Tuple,
      pad: _torch.Tensor,
  ):
      """
      Method to deflatten a particular weight partition.
      """
      permute = self.cluster_permute
  
      if pad is not None:
          weight_partition = _torch.cat(
              [weight_partition.flatten(), pad]
          )
  
      if permute and len(permute) == len(target_size):
          cur_shape = [target_size[i] for i in permute]
  
          weight_partition = weight_partition.reshape(cur_shape)
          weight_partition = weight_partition.permute(
              _torch.argsort(_torch.Tensor(permute)).tolist()
          )
          assert weight_partition.size() == target_size
  
      return weight_partition.reshape(target_size)
  
file_path: coremltools/coremltools/optimize/torch/palettization/_partitioner.py
imports: from typing import Tuple
  import torch
  from ._efficient_kmeans import _EfficientKMeans
label_desc: Use image node_id 6 for calling the _Partitioner obj's underlying member method code with example usage: obj.deflatten(weight_partition, target_size, pad) and returns: weight_partition