code: def reshape_batch_channel_to_channel_dim(
      x: Tensor, batch_size: int
  ) -> Tensor:
      """
      Detaches batch and channel dimensions.
  
      Args:
          x: input of shape (B*C,1,H,W,2) for 2D data or (B*C,1,H,W,D,2) for 3D data
          batch_size: batch size
  
      Returns:
          output of shape (B,C,...)
      """
      if len(x.shape) == 5:  # this is 2D
          bc, one, h, w, two = x.shape  # bc represents B*C
          c = bc // batch_size
          return x.view(batch_size, c, h, w, two)
  
      elif len(x.shape) == 6:  # this is 3D
          bc, one, h, w, d, two = x.shape  # bc represents B*C
          c = bc // batch_size
          return x.view(batch_size, c, h, w, d, two)
  
      else:
          raise ValueError(
              f"only 2D (B*C,1,H,W,2) and 3D (B*C,1,H,W,D,2) data are supported but x has shape {x.shape}"
          )
  
file_path: MONAI/monai/apps/reconstruction/networks/nets/utils.py
imports: from __future__ import annotations
  import math
  from torch import Tensor
  from torch.nn import functional
  from monai.apps.reconstruction.complex_utils import complex_conj_t, complex_mul_t
  from monai.networks.blocks.fft_utils_t import fftn_centered_t, ifftn_centered_t
label_desc: Use image node_id 4 for calling a global function with example usage: reshape_batch_channel_to_channel_dim(x, batch_size) and returns: x, x