code: def sensitivity_map_expand(
      img: Tensor, sens_maps: Tensor, spatial_dims: int = 2
  ) -> Tensor:
      """
      Expands an image to its corresponding coil images based on the given sens_maps. Let's say there
      are C coils. This function multiples image img with each coil sensitivity map in sens_maps and stacks
      the resulting C coil images along the channel dimension which is reserved for coils.
  
      Args:
          img: 2D image (B,1,H,W,2) with the last dimension being 2 (for real/imaginary parts). 3D data will have
              the shape (B,1,H,W,D,2).
          sens_maps: Sensitivity maps for combining coil images. The shape is (B,C,H,W,2) for 2D data
              or (B,C,H,W,D,2) for 3D data (C denotes the coil dimension).
          spatial_dims: is 2 for 2D data and is 3 for 3D data
  
      Returns:
          Expansion of x to (B,C,H,W,2) for 2D data and (B,C,H,W,D,2) for 3D data. The output is transferred
              to the frequency domain to yield coil measurements.
      """
      return fftn_centered_t(
          complex_mul_t(img, sens_maps),
          spatial_dims=spatial_dims,
          is_complex=True,
      )
  
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 10 for calling a global function with example usage: sensitivity_map_expand(img, sens_maps, spatial_dims) and returns: fftn_centered_t