class_docstr: This class defines the `MobileViTv2 <https://arxiv.org/abs/2206.02680>`_ block
  
  Args:
      opts: command line arguments
      in_channels (int): :math:`C_{in}` from an expected input of size :math:`(N, C_{in}, H, W)`
      attn_unit_dim (int): Input dimension to the attention unit
      ffn_multiplier (int): Expand the input dimensions by this factor in FFN. Default is 2.
      n_attn_blocks (Optional[int]): Number of attention units. Default: 2
      attn_dropout (Optional[float]): Dropout in multi-head attention. Default: 0.0
      dropout (Optional[float]): Dropout rate. Default: 0.0
      ffn_dropout (Optional[float]): Dropout between FFN layers in transformer. Default: 0.0
      patch_h (Optional[int]): Patch height for unfolding operation. Default: 8
      patch_w (Optional[int]): Patch width for unfolding operation. Default: 8
      conv_ksize (Optional[int]): Kernel size to learn local representations in MobileViT block. Default: 3
      dilation (Optional[int]): Dilation rate in convolutions. Default: 1
      attn_norm_layer (Optional[str]): Normalization layer in the attention block. Default: layer_norm_2d
code: def folding_coreml(
      self, patches: Tensor, output_size: Tuple[int, int]
  ) -> Tensor:
      # col2im is not supported on coreml, so tracing fails
      # We hack folding function via pixel_shuffle to enable coreml tracing
      batch_size, in_dim, patch_size, n_patches = patches.shape
  
      n_patches_h = output_size[0] // self.patch_h
      n_patches_w = output_size[1] // self.patch_w
  
      feature_map = patches.reshape(
          batch_size,
          in_dim * self.patch_h * self.patch_w,
          n_patches_h,
          n_patches_w,
      )
      assert (
          self.patch_h == self.patch_w
      ), "For Coreml, we need patch_h and patch_w are the same"
      feature_map = F.pixel_shuffle(
          feature_map, upscale_factor=self.patch_h
      )
      return feature_map
  
imports: import math
  from typing import Dict, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  from torch import Tensor, nn
  from torch.nn import functional
  from cvnets.layers import ConvLayer2d, get_normalization_layer
  from cvnets.modules.base_module import BaseModule
  from cvnets.modules.transformer import LinearAttnFFN, TransformerEncoder
label_desc: Use image node_id 8 for calling the MobileViTBlockv2 obj's underlying member method code with example usage: obj.folding_coreml(patches, output_size) and returns: feature_map