class_docstr: This class defines the `MobileViT block <https://arxiv.org/abs/2110.02178?context=cs.LG>`_
  
  Args:
      opts: command line arguments
      in_channels (int): :math:`C_{in}` from an expected input of size :math:`(N, C_{in}, H, W)`
      transformer_dim (int): Input dimension to the transformer unit
      ffn_dim (int): Dimension of the FFN block
      n_transformer_blocks (Optional[int]): Number of transformer blocks. Default: 2
      head_dim (Optional[int]): Head dimension in the multi-head attention. Default: 32
      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
      transformer_norm_layer (Optional[str]): Normalization layer in the transformer block. Default: layer_norm
      conv_ksize (Optional[int]): Kernel size to learn local representations in MobileViT block. Default: 3
      dilation (Optional[int]): Dilation rate in convolutions. Default: 1
      no_fusion (Optional[bool]): Do not combine the input and output feature maps. Default: False
code: def folding(self, patches: Tensor, info_dict: Dict) -> Tensor:
      n_dim = patches.dim()
      assert (
          n_dim == 3
      ), "Tensor should be of shape BPxNxC. Got: {}".format(
          patches.shape
      )
      # [BP, N, C] --> [B, P, N, C]
      patches = patches.contiguous().view(
          info_dict["batch_size"],
          self.patch_area,
          info_dict["total_patches"],
          -1,
      )
  
      batch_size, pixels, num_patches, channels = patches.size()
      num_patch_h = info_dict["num_patches_h"]
      num_patch_w = info_dict["num_patches_w"]
  
      # [B, P, N, C] --> [B, C, N, P]
      patches = patches.transpose(1, 3)
  
      # [B, C, N, P] --> [B*C*n_h, n_w, p_h, p_w]
      feature_map = patches.reshape(
          batch_size * channels * num_patch_h,
          num_patch_w,
          self.patch_h,
          self.patch_w,
      )
      # [B*C*n_h, n_w, p_h, p_w] --> [B*C*n_h, p_h, n_w, p_w]
      feature_map = feature_map.transpose(1, 2)
      # [B*C*n_h, p_h, n_w, p_w] --> [B, C, H, W]
      feature_map = feature_map.reshape(
          batch_size,
          channels,
          num_patch_h * self.patch_h,
          num_patch_w * self.patch_w,
      )
      if info_dict["interpolate"]:
          feature_map = F.interpolate(
              feature_map,
              size=info_dict["orig_size"],
              mode="bilinear",
              align_corners=False,
          )
      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 4 for calling the MobileViTBlock obj's underlying member method code with example usage: obj.folding(patches, info_dict) and returns: feature_map