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 __repr__(self) -> str:
      repr_str = "{}(".format(self.__class__.__name__)
  
      repr_str += "\n\t Local representations"
      if isinstance(self.local_rep, nn.Sequential):
          for m in self.local_rep:
              repr_str += "\n\t\t {}".format(m)
      else:
          repr_str += "\n\t\t {}".format(self.local_rep)
  
      repr_str += (
          "\n\t Global representations with patch size of {}x{}".format(
              self.patch_h,
              self.patch_w,
          )
      )
      if isinstance(self.global_rep, nn.Sequential):
          for m in self.global_rep:
              repr_str += "\n\t\t {}".format(m)
      else:
          repr_str += "\n\t\t {}".format(self.global_rep)
  
      if isinstance(self.conv_proj, nn.Sequential):
          for m in self.conv_proj:
              repr_str += "\n\t\t {}".format(m)
      else:
          repr_str += "\n\t\t {}".format(self.conv_proj)
  
      repr_str += "\n)"
      return repr_str
  
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 MobileViTBlockv2 obj's underlying member method code with example usage: obj.__repr__() and returns: repr_str