class_docstr: An upsampling module that can be used for UNETR: "Hatamizadeh et al.,
  UNETR: Transformers for 3D Medical Image Segmentation <https://arxiv.org/abs/2103.10504>"
class_docstr_tok: ["An","upsampling","module","that","can","be","used","for","UNETR",":","``","Hatamizadeh","et","al.",",","UNETR",":","Transformers","for","3D","Medical","Image","Segmentation","<","https",":","\/\/arxiv.org\/abs\/2103.10504",">","''"]
code: def forward(self, inp, skip):
      # number of channels for skip should equals to out_channels
      out = self.transp_conv(inp)
      out = torch.cat((out, skip), dim=1)
      out = self.conv_block(out)
      return out
  
file_path: MONAI/monai/networks/blocks/unetr_block.py
imports: from __future__ import annotations
  from collections.abc import Sequence
  import torch
  import torch.nn
  from monai.networks.blocks.dynunet_block import UnetBasicBlock, UnetResBlock, get_conv_layer
label_desc: Use image node_id 2 for calling the UnetrUpBlock obj's underlying member method code with example usage: obj.forward(inp, skip) and returns: out