class_docstr: This class uses a convolutional model to learn coil sensitivity maps for multi-coil MRI reconstruction.
  The convolutional model is :py:class:`monai.apps.reconstruction.networks.nets.complex_unet` by default
  but can be specified by the user as well. Learning is done on the center of the under-sampled
  kspace (that region is fully sampled).
  
  The data being a (complex) 2-channel tensor is a requirement for using this model.
  
  Modified and adopted from: https://github.com/facebookresearch/fastMRI
  
  Args:
      spatial_dims: number of spatial dimensions.
      features: six integers as numbers of features. denotes number of channels in each layer.
      act: activation type and arguments. Defaults to LeakyReLU.
      norm: feature normalization type and arguments. Defaults to instance norm.
      bias: whether to have a bias term in convolution blocks. Defaults to True.
      dropout: dropout ratio. Defaults to 0.0.
      upsample: upsampling mode, available options are
          ``"deconv"``, ``"pixelshuffle"``, ``"nontrainable"``.
      coil_dim: coil dimension in the data
      conv_net: the learning model used to estimate the coil sensitivity maps. default
          is :py:class:`monai.apps.reconstruction.networks.nets.complex_unet`. The only
          requirement on the model is to have 2 as input and output number of channels.
docstr: Extracts the size of the fully-sampled part of the kspace. Note that when a kspace
  is under-sampled, a part of its center is fully sampled. This part is called the Auto
  Calibration Region (ACR). ACR is used for sensitivity map computation.
  
  Args:
      mask: the under-sampling mask of shape (..., S, 1) where S denotes the sampling dimension
  
  Returns:
      A tuple containing
          (1) left index of the region
          (2) right index of the region
  
  Note:
      Suppose the mask is of shape (1,1,20,1). If this function returns 8,12 as left and right
          indices, then it means that the fully-sampled center region has size 4 starting from 8 to 12.
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code: def get_fully_sampled_region(self, mask: Tensor) -> tuple[int, int]:
      """
      Extracts the size of the fully-sampled part of the kspace. Note that when a kspace
      is under-sampled, a part of its center is fully sampled. This part is called the Auto
      Calibration Region (ACR). ACR is used for sensitivity map computation.
  
      Args:
          mask: the under-sampling mask of shape (..., S, 1) where S denotes the sampling dimension
  
      Returns:
          A tuple containing
              (1) left index of the region
              (2) right index of the region
  
      Note:
          Suppose the mask is of shape (1,1,20,1). If this function returns 8,12 as left and right
              indices, then it means that the fully-sampled center region has size 4 starting from 8 to 12.
      """
      left = right = mask.shape[-2] // 2
      while mask[..., right, :]:
          right += 1
  
      while mask[..., left, :]:
          left -= 1
  
      return left + 1, right
  
file_path: MONAI/monai/apps/reconstruction/networks/nets/coil_sensitivity_model.py
imports: from __future__ import annotations
  from collections.abc import Sequence
  import torch
  import torch.nn
  from torch import Tensor
  from monai.apps.reconstruction.mri_utils import root_sum_of_squares_t
  from monai.apps.reconstruction.networks.nets.complex_unet import ComplexUnet
  from monai.apps.reconstruction.networks.nets.utils import reshape_batch_channel_to_channel_dim, reshape_channel_to_batch_dim
  from monai.networks.blocks.fft_utils_t import ifftn_centered_t
label_desc: Use image node_id 2 for calling the CoilSensitivityModel obj's underlying member method code with example usage: obj.get_fully_sampled_region(mask) and returns: unknown,  right