code: # Copyright (c) MONAI Consortium
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #     http://www.apache.org/licenses/LICENSE-2.0
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """
  A collection of "vanilla" transforms for spatial operations
  https://github.com/Project-MONAI/MONAI/wiki/MONAI_Design
  """
  import warnings
  from copy import deepcopy
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
  
  import numpy as np
  import torch
  
  from monai.config import USE_COMPILED, DtypeLike
  from monai.config.type_definitions import NdarrayOrTensor
  from monai.data.utils import AFFINE_TOL, compute_shape_offset, reorient_spatial_axes, to_affine_nd, zoom_affine
  from monai.networks.layers import AffineTransform, GaussianFilter, grid_pull
  from monai.networks.utils import meshgrid_ij, normalize_transform
  from monai.transforms.croppad.array import CenterSpatialCrop, Pad
  from monai.transforms.transform import Randomizable, RandomizableTransform, ThreadUnsafe, Transform
  from monai.transforms.utils import (
      create_control_grid,
      create_grid,
      create_rotate,
      create_scale,
      create_shear,
      create_translate,
      map_spatial_axes,
  )
  from monai.transforms.utils_pytorch_numpy_unification import allclose, moveaxis
  from monai.utils import (
      GridSampleMode,
      GridSamplePadMode,
      InterpolateMode,
      NumpyPadMode,
      PytorchPadMode,
      ensure_tuple,
      ensure_tuple_rep,
      ensure_tuple_size,
      fall_back_tuple,
      issequenceiterable,
      optional_import,
      pytorch_after,
  )
  from monai.utils.deprecate_utils import deprecated_arg
  from monai.utils.enums import TransformBackends
  from monai.utils.module import look_up_option
  from monai.utils.type_conversion import convert_data_type, convert_to_dst_type
  
  nib, has_nib = optional_import("nibabel")
  
  __all__ = [
      "SpatialResample",
      "ResampleToMatch",
      "Spacing",
      "Orientation",
      "Flip",
      "GridDistortion",
      "Resize",
      "Rotate",
      "Zoom",
      "Rotate90",
      "RandRotate90",
      "RandRotate",
      "RandFlip",
      "RandGridDistortion",
      "RandAxisFlip",
      "RandZoom",
      "AffineGrid",
      "RandAffineGrid",
      "RandDeformGrid",
      "Resample",
      "Affine",
      "RandAffine",
      "Rand2DElastic",
      "Rand3DElastic",
  ]
  
  RandRange = Optional[Union[Sequence[Union[Tuple[float, float], float]], float]]
  
  
  class SpatialResample(Transform):
      """
      Resample input image from the orientation/spacing defined by ``src_affine`` affine matrix into
      the ones specified by ``dst_affine`` affine matrix.
  
      Internally this transform computes the affine transform matrix from ``src_affine`` to ``dst_affine``,
      by ``xform = linalg.solve(src_affine, dst_affine)``, and call ``monai.transforms.Affine`` with ``xform``.
      """
  
      backend = [TransformBackends.TORCH]
  
      def __init__(
          self,
          mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
          padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
          align_corners: bool = False,
          dtype: DtypeLike = np.float64,
      ):
          """
          Args:
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
                  When `USE_COMPILED` is `True`, this argument uses
                  ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations.
                  See also: https://docs.monai.io/en/stable/networks.html#grid-pull
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``"border"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              align_corners: Geometrically, we consider the pixels of the input as squares rather than points.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.
                  If ``None``, use the data type of input data. To be compatible with other modules,
                  the output data type is always ``np.float32``.
          """
          self.mode = mode
          self.padding_mode = padding_mode
          self.align_corners = align_corners
          self.dtype = dtype
  
      def __call__(
          self,
          img: NdarrayOrTensor,
          src_affine: Optional[NdarrayOrTensor] = None,
          dst_affine: Optional[NdarrayOrTensor] = None,
          spatial_size: Optional[Union[Sequence[int], np.ndarray, int]] = None,
          mode: Union[GridSampleMode, str, None] = GridSampleMode.BILINEAR,
          padding_mode: Union[GridSamplePadMode, str, None] = GridSamplePadMode.BORDER,
          align_corners: Optional[bool] = False,
          dtype: DtypeLike = None,
      ) -> Tuple[NdarrayOrTensor, NdarrayOrTensor]:
          """
          Args:
              img: input image to be resampled. It currently supports channel-first arrays with
                  at most three spatial dimensions.
              src_affine: source affine matrix. Defaults to ``None``, which means the identity matrix.
                  the shape should be `(r+1, r+1)` where `r` is the spatial rank of ``img``.
              dst_affine: destination affine matrix. Defaults to ``None``, which means the same as `src_affine`.
                  the shape should be `(r+1, r+1)` where `r` is the spatial rank of ``img``.
                  when `dst_affine` and `spatial_size` are None, the input will be returned without resampling,
                  but the data type will be `float32`.
              spatial_size: output image spatial size.
                  if `spatial_size` and `self.spatial_size` are not defined,
                  the transform will compute a spatial size automatically containing the previous field of view.
                  if `spatial_size` is ``-1`` are the transform will use the corresponding input img size.
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
                  When `USE_COMPILED` is `True`, this argument uses
                  ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations.
                  See also: https://docs.monai.io/en/stable/networks.html#grid-pull
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``"border"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              align_corners: Geometrically, we consider the pixels of the input as squares rather than points.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              dtype: data type for resampling computation. Defaults to ``self.dtype`` or
                  ``np.float64`` (for best precision). If ``None``, use the data type of input data.
                  To be compatible with other modules, the output data type is always `float32`.
  
          The spatial rank is determined by the smallest among ``img.ndim -1``, ``len(src_affine) - 1``, and ``3``.
  
          When both ``monai.config.USE_COMPILED`` and ``align_corners`` are set to ``True``,
          MONAI's resampling implementation will be used.
          Set `dst_affine` and `spatial_size` to `None` to turn off the resampling step.
          """
          if src_affine is None:
              src_affine = np.eye(4, dtype=np.float64)
          spatial_rank = min(len(img.shape) - 1, src_affine.shape[0] - 1, 3)
          if (not isinstance(spatial_size, int) or spatial_size != -1) and spatial_size is not None:
              spatial_rank = min(len(ensure_tuple(spatial_size)), 3)  # infer spatial rank based on spatial_size
          src_affine = to_affine_nd(spatial_rank, src_affine)
          dst_affine = to_affine_nd(spatial_rank, dst_affine) if dst_affine is not None else src_affine
          dst_affine, *_ = convert_to_dst_type(dst_affine, dst_affine, dtype=torch.float32)
  
          in_spatial_size = np.asarray(img.shape[1 : spatial_rank + 1])
          if isinstance(spatial_size, int) and (spatial_size == -1):  # using the input spatial size
              spatial_size = in_spatial_size
          elif spatial_size is None and spatial_rank > 1:  # auto spatial size
              spatial_size, _ = compute_shape_offset(in_spatial_size, src_affine, dst_affine)  # type: ignore
          spatial_size = np.asarray(fall_back_tuple(ensure_tuple(spatial_size)[:spatial_rank], in_spatial_size))
  
          if (
              allclose(src_affine, dst_affine, atol=AFFINE_TOL)
              and allclose(spatial_size, in_spatial_size)
              or spatial_rank == 1
          ):
              # no significant change, return original image
              output_data, *_ = convert_to_dst_type(img, img, dtype=torch.float32)
              return output_data, dst_affine
  
          if has_nib and isinstance(img, np.ndarray):
              spatial_ornt, dst_r = reorient_spatial_axes(img.shape[1 : spatial_rank + 1], src_affine, dst_affine)
              if allclose(dst_r, dst_affine, atol=AFFINE_TOL) and allclose(spatial_size, in_spatial_size):
                  # simple reorientation achieves the desired affine
                  spatial_ornt[:, 0] += 1
                  spatial_ornt = np.concatenate([np.array([[0, 1]]), spatial_ornt])
                  img_ = nib.orientations.apply_orientation(img, spatial_ornt)
                  output_data, *_ = convert_to_dst_type(img_, img, dtype=torch.float32)
                  return output_data, dst_affine
  
          try:
              src_affine, *_ = convert_to_dst_type(src_affine, dst_affine)
              if isinstance(src_affine, np.ndarray):
                  xform = np.linalg.solve(src_affine, dst_affine)
              else:
                  xform = (
                      torch.linalg.solve(src_affine, dst_affine)
                      if pytorch_after(1, 8, 0)
                      else torch.solve(dst_affine, src_affine).solution  # type: ignore
                  )
          except (np.linalg.LinAlgError, RuntimeError) as e:
              raise ValueError(f"src affine is not invertible: {src_affine}") from e
          xform = to_affine_nd(spatial_rank, xform)
          # no resampling if it's identity transform
          if allclose(xform, np.diag(np.ones(len(xform))), atol=AFFINE_TOL) and allclose(spatial_size, in_spatial_size):
              output_data, *_ = convert_to_dst_type(img, img, dtype=torch.float32)
              return output_data, dst_affine
  
          _dtype = dtype or self.dtype or img.dtype
          in_spatial_size = in_spatial_size.tolist()
          chns, additional_dims = img.shape[0], img.shape[spatial_rank + 1 :]  # beyond three spatial dims
          # resample
          img_ = convert_data_type(img, torch.Tensor, dtype=_dtype)[0]
          xform = convert_to_dst_type(xform, img_)[0]
          align_corners = self.align_corners if align_corners is None else align_corners
          mode = mode or self.mode
          padding_mode = padding_mode or self.padding_mode
          if additional_dims:
              xform_shape = [-1] + in_spatial_size
              img_ = img_.reshape(xform_shape)
          if align_corners:
              _t_r = torch.diag(torch.ones(len(xform), dtype=xform.dtype, device=xform.device))  # type: ignore
              for idx, d_dst in enumerate(spatial_size[:spatial_rank]):
                  _t_r[idx, -1] = (max(d_dst, 2) - 1.0) / 2.0
              xform = xform @ _t_r
              if not USE_COMPILED:
                  _t_l = normalize_transform(
                      in_spatial_size, xform.device, xform.dtype, align_corners=True  # type: ignore
                  )
                  xform = _t_l @ xform  # type: ignore
              affine_xform = Affine(
                  affine=xform, spatial_size=spatial_size, norm_coords=False, image_only=True, dtype=_dtype
              )
              output_data = affine_xform(img_, mode=mode, padding_mode=padding_mode)
          else:
              affine_xform = AffineTransform(
                  normalized=False,
                  mode=mode,
                  padding_mode=padding_mode,
                  align_corners=align_corners,
                  reverse_indexing=True,
              )
              output_data = affine_xform(img_.unsqueeze(0), theta=xform, spatial_size=spatial_size).squeeze(0)
          if additional_dims:
              full_shape = (chns, *spatial_size, *additional_dims)
              output_data = output_data.reshape(full_shape)
          # output dtype float
          output_data, *_ = convert_to_dst_type(output_data, img, dtype=torch.float32)
          return output_data, dst_affine
  
  
  class ResampleToMatch(SpatialResample):
      """Resample an image to match given meta data. The affine matrix will be aligned,
      and the size of the output image will match."""
  
      def __call__(  # type: ignore
          self,
          img: NdarrayOrTensor,
          src_meta: Optional[Dict] = None,
          dst_meta: Optional[Dict] = None,
          mode: Union[GridSampleMode, str, None] = GridSampleMode.BILINEAR,
          padding_mode: Union[GridSamplePadMode, str, None] = GridSamplePadMode.BORDER,
          align_corners: Optional[bool] = False,
          dtype: DtypeLike = None,
      ):
          if src_meta is None:
              raise RuntimeError("`in_meta` is missing")
          if dst_meta is None:
              raise RuntimeError("`out_meta` is missing")
          mode = mode or self.mode
          padding_mode = padding_mode or self.padding_mode
          align_corners = self.align_corners if align_corners is None else align_corners
          dtype = dtype or self.dtype
          src_affine = src_meta.get("affine")
          dst_affine = dst_meta.get("affine")
          img, updated_affine = super().__call__(
              img=img,
              src_affine=src_affine,
              dst_affine=dst_affine,
              spatial_size=dst_meta.get("spatial_shape"),
              mode=mode,
              padding_mode=padding_mode,
              align_corners=align_corners,
              dtype=dtype,
          )
          dst_meta = deepcopy(dst_meta)
          dst_meta["affine"] = updated_affine
          return img, dst_meta
  
  
  class Spacing(Transform):
      """
      Resample input image into the specified `pixdim`.
      """
  
      backend = SpatialResample.backend
  
      def __init__(
          self,
          pixdim: Union[Sequence[float], float, np.ndarray],
          diagonal: bool = False,
          mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
          padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
          align_corners: bool = False,
          dtype: DtypeLike = np.float64,
          image_only: bool = False,
      ) -> None:
          """
          Args:
              pixdim: output voxel spacing. if providing a single number, will use it for the first dimension.
                  items of the pixdim sequence map to the spatial dimensions of input image, if length
                  of pixdim sequence is longer than image spatial dimensions, will ignore the longer part,
                  if shorter, will pad with `1.0`.
                  if the components of the `pixdim` are non-positive values, the transform will use the
                  corresponding components of the original pixdim, which is computed from the `affine`
                  matrix of input image.
              diagonal: whether to resample the input to have a diagonal affine matrix.
                  If True, the input data is resampled to the following affine::
  
                      np.diag((pixdim_0, pixdim_1, ..., pixdim_n, 1))
  
                  This effectively resets the volume to the world coordinate system (RAS+ in nibabel).
                  The original orientation, rotation, shearing are not preserved.
  
                  If False, this transform preserves the axes orientation, orthogonal rotation and
                  translation components from the original affine. This option will not flip/swap axes
                  of the original data.
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
                  When `USE_COMPILED` is `True`, this argument uses
                  ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations.
                  See also: https://docs.monai.io/en/stable/networks.html#grid-pull
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``"border"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              align_corners: Geometrically, we consider the pixels of the input as squares rather than points.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.
                  If None, use the data type of input data. To be compatible with other modules,
                  the output data type is always ``np.float32``.
              image_only: return just the image or the image, the old affine and new affine. Default is `False`.
  
          """
          self.pixdim = np.array(ensure_tuple(pixdim), dtype=np.float64)
          self.diagonal = diagonal
          self.image_only = image_only
  
          self.sp_resample = SpatialResample(
              mode=look_up_option(mode, GridSampleMode),
              padding_mode=look_up_option(padding_mode, GridSamplePadMode),
              align_corners=align_corners,
              dtype=dtype,
          )
  
      def __call__(
          self,
          data_array: NdarrayOrTensor,
          affine: Optional[NdarrayOrTensor] = None,
          mode: Optional[Union[GridSampleMode, str]] = None,
          padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
          align_corners: Optional[bool] = None,
          dtype: DtypeLike = None,
          output_spatial_shape: Optional[Union[Sequence[int], np.ndarray, int]] = None,
      ) -> Union[NdarrayOrTensor, Tuple[NdarrayOrTensor, NdarrayOrTensor, NdarrayOrTensor]]:
          """
          Args:
              data_array: in shape (num_channels, H[, W, ...]).
              affine (matrix): (N+1)x(N+1) original affine matrix for spatially ND `data_array`. Defaults to identity.
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``self.mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
                  When `USE_COMPILED` is `True`, this argument uses
                  ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations.
                  See also: https://docs.monai.io/en/stable/networks.html#grid-pull
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              align_corners: Geometrically, we consider the pixels of the input as squares rather than points.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              dtype: data type for resampling computation. Defaults to ``self.dtype``.
                  If None, use the data type of input data. To be compatible with other modules,
                  the output data type is always ``np.float32``.
              output_spatial_shape: specify the shape of the output data_array. This is typically useful for
                  the inverse of `Spacingd` where sometimes we could not compute the exact shape due to the quantization
                  error with the affine.
  
          Raises:
              ValueError: When ``data_array`` has no spatial dimensions.
              ValueError: When ``pixdim`` is nonpositive.
  
          Returns:
              data_array (resampled into `self.pixdim`), original affine, current affine.
  
          """
          sr = int(data_array.ndim - 1)
          if sr <= 0:
              raise ValueError("data_array must have at least one spatial dimension.")
          if affine is None:
              # default to identity
              affine_np = affine = np.eye(sr + 1, dtype=np.float64)
              affine_ = np.eye(sr + 1, dtype=np.float64)
          else:
              affine_np, *_ = convert_data_type(affine, np.ndarray)
              affine_ = to_affine_nd(sr, affine_np)
  
          out_d = self.pixdim[:sr]
          if out_d.size < sr:
              out_d = np.append(out_d, [1.0] * (sr - out_d.size))
  
          # compute output affine, shape and offset
          new_affine = zoom_affine(affine_, out_d, diagonal=self.diagonal)
          output_shape, offset = compute_shape_offset(data_array.shape[1:], affine_, new_affine)
          new_affine[:sr, -1] = offset[:sr]
          output_data, new_affine = self.sp_resample(
              data_array,
              src_affine=affine,
              dst_affine=new_affine,
              spatial_size=list(output_shape) if output_spatial_shape is None else output_spatial_shape,
              mode=mode,
              padding_mode=padding_mode,
              align_corners=align_corners,
              dtype=dtype,
          )
          new_affine = to_affine_nd(affine_np, new_affine)
          new_affine, *_ = convert_to_dst_type(src=new_affine, dst=affine, dtype=torch.float32)
  
          if self.image_only:
              return output_data
          return output_data, affine, new_affine
  
  
  class Orientation(Transform):
      """
      Change the input image's orientation into the specified based on `axcodes`.
      """
  
      backend = [TransformBackends.NUMPY, TransformBackends.TORCH]
  
      def __init__(
          self,
          axcodes: Optional[str] = None,
          as_closest_canonical: bool = False,
          labels: Optional[Sequence[Tuple[str, str]]] = tuple(zip("LPI", "RAS")),
          image_only: bool = False,
      ) -> None:
          """
          Args:
              axcodes: N elements sequence for spatial ND input's orientation.
                  e.g. axcodes='RAS' represents 3D orientation:
                  (Left, Right), (Posterior, Anterior), (Inferior, Superior).
                  default orientation labels options are: 'L' and 'R' for the first dimension,
                  'P' and 'A' for the second, 'I' and 'S' for the third.
              as_closest_canonical: if True, load the image as closest to canonical axis format.
              labels: optional, None or sequence of (2,) sequences
                  (2,) sequences are labels for (beginning, end) of output axis.
                  Defaults to ``(('L', 'R'), ('P', 'A'), ('I', 'S'))``.
              image_only: if True return only the image volume, otherwise return (image, affine, new_affine).
  
          Raises:
              ValueError: When ``axcodes=None`` and ``as_closest_canonical=True``. Incompatible values.
  
          See Also: `nibabel.orientations.ornt2axcodes`.
  
          """
          if axcodes is None and not as_closest_canonical:
              raise ValueError("Incompatible values: axcodes=None and as_closest_canonical=True.")
          if axcodes is not None and as_closest_canonical:
              warnings.warn("using as_closest_canonical=True, axcodes ignored.")
          self.axcodes = axcodes
          self.as_closest_canonical = as_closest_canonical
          self.labels = labels
          self.image_only = image_only
  
      def __call__(
          self, data_array: NdarrayOrTensor, affine: Optional[NdarrayOrTensor] = None
      ) -> Union[NdarrayOrTensor, Tuple[NdarrayOrTensor, NdarrayOrTensor, NdarrayOrTensor]]:
          """
          original orientation of `data_array` is defined by `affine`.
  
          Args:
              data_array: in shape (num_channels, H[, W, ...]).
              affine (matrix): (N+1)x(N+1) original affine matrix for spatially ND `data_array`. Defaults to identity.
  
          Raises:
              ValueError: When ``data_array`` has no spatial dimensions.
              ValueError: When ``axcodes`` spatiality differs from ``data_array``.
  
          Returns:
              data_array [reoriented in `self.axcodes`] if `self.image_only`, else
              (data_array [reoriented in `self.axcodes`], original axcodes, current axcodes).
  
          """
          spatial_shape = data_array.shape[1:]
          sr = len(spatial_shape)
          if sr <= 0:
              raise ValueError("data_array must have at least one spatial dimension.")
          affine_: np.ndarray
          if affine is None:
              # default to identity
              affine_np = affine = np.eye(sr + 1, dtype=np.float64)
              affine_ = np.eye(sr + 1, dtype=np.float64)
          else:
              affine_np, *_ = convert_data_type(affine, np.ndarray)
              affine_ = to_affine_nd(sr, affine_np)
  
          src = nib.io_orientation(affine_)
          if self.as_closest_canonical:
              spatial_ornt = src
          else:
              if self.axcodes is None:
                  raise ValueError("Incompatible values: axcodes=None and as_closest_canonical=True.")
              if sr < len(self.axcodes):
                  warnings.warn(
                      f"axcodes ('{self.axcodes}') length is smaller than the number of input spatial dimensions D={sr}.\n"
                      f"{self.__class__.__name__}: input spatial shape is {spatial_shape}, num. channels is {data_array.shape[0]},"
                      "please make sure the input is in the channel-first format."
                  )
              dst = nib.orientations.axcodes2ornt(self.axcodes[:sr], labels=self.labels)
              if len(dst) < sr:
                  raise ValueError(
                      f"axcodes must match data_array spatially, got axcodes={len(self.axcodes)}D data_array={sr}D"
                  )
              spatial_ornt = nib.orientations.ornt_transform(src, dst)
          new_affine = affine_ @ nib.orientations.inv_ornt_aff(spatial_ornt, spatial_shape)
          _is_tensor = isinstance(data_array, torch.Tensor)
          spatial_ornt[:, 0] += 1  # skip channel dim
          spatial_ornt = np.concatenate([np.array([[0, 1]]), spatial_ornt])
          axes = [ax for ax, flip in enumerate(spatial_ornt[:, 1]) if flip == -1]
          if axes:
              data_array = (
                  torch.flip(data_array, dims=axes) if _is_tensor else np.flip(data_array, axis=axes)  # type: ignore
              )
          full_transpose = np.arange(len(data_array.shape))
          full_transpose[: len(spatial_ornt)] = np.argsort(spatial_ornt[:, 0])
          if not np.all(full_transpose == np.arange(len(data_array.shape))):
              if _is_tensor:
                  data_array = data_array.permute(full_transpose.tolist())  # type: ignore
              else:
                  data_array = data_array.transpose(full_transpose)  # type: ignore
          out, *_ = convert_to_dst_type(src=data_array, dst=data_array)
          new_affine = to_affine_nd(affine_np, new_affine)
          new_affine, *_ = convert_to_dst_type(src=new_affine, dst=affine, dtype=torch.float32)
  
          if self.image_only:
              return out
          return out, affine, new_affine
  
  
  class Flip(Transform):
      """
      Reverses the order of elements along the given spatial axis. Preserves shape.
      Uses ``np.flip`` in practice. See numpy.flip for additional details:
      https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html.
  
      Args:
          spatial_axis: spatial axes along which to flip over. Default is None.
              The default `axis=None` will flip over all of the axes of the input array.
              If axis is negative it counts from the last to the first axis.
              If axis is a tuple of ints, flipping is performed on all of the axes
              specified in the tuple.
  
      """
  
      backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
  
      def __init__(self, spatial_axis: Optional[Union[Sequence[int], int]] = None) -> None:
          self.spatial_axis = spatial_axis
  
      def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
          """
          Args:
              img: channel first array, must have shape: (num_channels, H[, W, ..., ]),
          """
          if isinstance(img, np.ndarray):
              return np.ascontiguousarray(np.flip(img, map_spatial_axes(img.ndim, self.spatial_axis)))
          return torch.flip(img, map_spatial_axes(img.ndim, self.spatial_axis))
  
  
  class Resize(Transform):
      """
      Resize the input image to given spatial size (with scaling, not cropping/padding).
      Implemented using :py:class:`torch.nn.functional.interpolate`.
  
      Args:
          spatial_size: expected shape of spatial dimensions after resize operation.
              if some components of the `spatial_size` are non-positive values, the transform will use the
              corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted
              to `(32, 64)` if the second spatial dimension size of img is `64`.
          size_mode: should be "all" or "longest", if "all", will use `spatial_size` for all the spatial dims,
              if "longest", rescale the image so that only the longest side is equal to specified `spatial_size`,
              which must be an int number in this case, keeping the aspect ratio of the initial image, refer to:
              https://albumentations.ai/docs/api_reference/augmentations/geometric/resize/
              #albumentations.augmentations.geometric.resize.LongestMaxSize.
          mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}
              The interpolation mode. Defaults to ``"area"``.
              See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
          align_corners: This only has an effect when mode is
              'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None.
              See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
      """
  
      backend = [TransformBackends.TORCH]
  
      def __init__(
          self,
          spatial_size: Union[Sequence[int], int],
          size_mode: str = "all",
          mode: Union[InterpolateMode, str] = InterpolateMode.AREA,
          align_corners: Optional[bool] = None,
      ) -> None:
          self.size_mode = look_up_option(size_mode, ["all", "longest"])
          self.spatial_size = spatial_size
          self.mode: InterpolateMode = look_up_option(mode, InterpolateMode)
          self.align_corners = align_corners
  
      def __call__(
          self,
          img: NdarrayOrTensor,
          mode: Optional[Union[InterpolateMode, str]] = None,
          align_corners: Optional[bool] = None,
      ) -> NdarrayOrTensor:
          """
          Args:
              img: channel first array, must have shape: (num_channels, H[, W, ..., ]).
              mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}
                  The interpolation mode. Defaults to ``self.mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
              align_corners: This only has an effect when mode is
                  'linear', 'bilinear', 'bicubic' or 'trilinear'. Defaults to ``self.align_corners``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
  
          Raises:
              ValueError: When ``self.spatial_size`` length is less than ``img`` spatial dimensions.
  
          """
          img_, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float)
          if self.size_mode == "all":
              input_ndim = img_.ndim - 1  # spatial ndim
              output_ndim = len(ensure_tuple(self.spatial_size))
              if output_ndim > input_ndim:
                  input_shape = ensure_tuple_size(img_.shape, output_ndim + 1, 1)
                  img_ = img_.reshape(input_shape)
              elif output_ndim < input_ndim:
                  raise ValueError(
                      "len(spatial_size) must be greater or equal to img spatial dimensions, "
                      f"got spatial_size={output_ndim} img={input_ndim}."
                  )
              spatial_size_ = fall_back_tuple(self.spatial_size, img_.shape[1:])
          else:  # for the "longest" mode
              img_size = img_.shape[1:]
              if not isinstance(self.spatial_size, int):
                  raise ValueError("spatial_size must be an int number if size_mode is 'longest'.")
              scale = self.spatial_size / max(img_size)
              spatial_size_ = tuple(int(round(s * scale)) for s in img_size)
          resized = torch.nn.functional.interpolate(
              input=img_.unsqueeze(0),
              size=spatial_size_,
              mode=look_up_option(self.mode if mode is None else mode, InterpolateMode).value,
              align_corners=self.align_corners if align_corners is None else align_corners,
          )
          out, *_ = convert_to_dst_type(resized.squeeze(0), img)
          return out
  
  
  class Rotate(Transform, ThreadUnsafe):
      """
      Rotates an input image by given angle using :py:class:`monai.networks.layers.AffineTransform`.
  
      Args:
          angle: Rotation angle(s) in radians. should a float for 2D, three floats for 3D.
          keep_size: If it is True, the output shape is kept the same as the input.
              If it is False, the output shape is adapted so that the
              input array is contained completely in the output. Default is True.
          mode: {``"bilinear"``, ``"nearest"``}
              Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
              See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
          padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
              Padding mode for outside grid values. Defaults to ``"border"``.
              See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
          align_corners: Defaults to False.
              See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
          dtype: data type for resampling computation. Defaults to ``np.float32``.
              If None, use the data type of input data. To be compatible with other modules,
              the output data type is always ``np.float32``.
      """
  
      backend = [TransformBackends.TORCH]
  
      def __init__(
          self,
          angle: Union[Sequence[float], float],
          keep_size: bool = True,
          mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
          padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
          align_corners: bool = False,
          dtype: Union[DtypeLike, torch.dtype] = np.float32,
      ) -> None:
          self.angle = angle
          self.keep_size = keep_size
          self.mode: GridSampleMode = look_up_option(mode, GridSampleMode)
          self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode)
          self.align_corners = align_corners
          self.dtype = dtype
          self._rotation_matrix: Optional[NdarrayOrTensor] = None
  
      def __call__(
          self,
          img: NdarrayOrTensor,
          mode: Optional[Union[GridSampleMode, str]] = None,
          padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
          align_corners: Optional[bool] = None,
          dtype: Union[DtypeLike, torch.dtype] = None,
      ) -> NdarrayOrTensor:
          """
          Args:
              img: channel first array, must have shape: [chns, H, W] or [chns, H, W, D].
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``self.mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
                  align_corners: Defaults to ``self.align_corners``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              align_corners: Defaults to ``self.align_corners``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              dtype: data type for resampling computation. Defaults to ``self.dtype``.
                  If None, use the data type of input data. To be compatible with other modules,
                  the output data type is always ``np.float32``.
  
          Raises:
              ValueError: When ``img`` spatially is not one of [2D, 3D].
  
          """
          _dtype = dtype or self.dtype or img.dtype
  
          img_t, *_ = convert_data_type(img, torch.Tensor, dtype=_dtype)
  
          im_shape = np.asarray(img_t.shape[1:])  # spatial dimensions
          input_ndim = len(im_shape)
          if input_ndim not in (2, 3):
              raise ValueError(f"Unsupported img dimension: {input_ndim}, available options are [2, 3].")
          _angle = ensure_tuple_rep(self.angle, 1 if input_ndim == 2 else 3)
          transform = create_rotate(input_ndim, _angle)
          shift = create_translate(input_ndim, ((im_shape - 1) / 2).tolist())
          if self.keep_size:
              output_shape = im_shape
          else:
              corners = np.asarray(np.meshgrid(*[(0, dim) for dim in im_shape], indexing="ij")).reshape(
                  (len(im_shape), -1)
              )
              corners = transform[:-1, :-1] @ corners  # type: ignore
              output_shape = np.asarray(corners.ptp(axis=1) + 0.5, dtype=int)
          shift_1 = create_translate(input_ndim, (-(output_shape - 1) / 2).tolist())
          transform = shift @ transform @ shift_1
  
          transform_t, *_ = convert_to_dst_type(transform, img_t)
  
          xform = AffineTransform(
              normalized=False,
              mode=look_up_option(mode or self.mode, GridSampleMode),
              padding_mode=look_up_option(padding_mode or self.padding_mode, GridSamplePadMode),
              align_corners=self.align_corners if align_corners is None else align_corners,
              reverse_indexing=True,
          )
          output: torch.Tensor = xform(img_t.unsqueeze(0), transform_t, spatial_size=output_shape).float().squeeze(0)
          self._rotation_matrix = transform
          out: NdarrayOrTensor
          out, *_ = convert_to_dst_type(output, dst=img, dtype=output.dtype)
          return out
  
      def get_rotation_matrix(self) -> Optional[NdarrayOrTensor]:
          """
          Get the most recently applied rotation matrix
          This is not thread-safe.
          """
          return self._rotation_matrix
  
  
  class Zoom(Transform):
      """
      Zooms an ND image using :py:class:`torch.nn.functional.interpolate`.
      For details, please see https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html.
  
      Different from :py:class:`monai.transforms.resize`, this transform takes scaling factors
      as input, and provides an option of preserving the input spatial size.
  
      Args:
          zoom: The zoom factor along the spatial axes.
              If a float, zoom is the same for each spatial axis.
              If a sequence, zoom should contain one value for each spatial axis.
          mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}
              The interpolation mode. Defaults to ``"area"``.
              See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
          padding_mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
              ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
              available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
              One of the listed string values or a user supplied function. Defaults to ``"constant"``.
              The mode to pad data after zooming.
              See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
              https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
          align_corners: This only has an effect when mode is
              'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None.
              See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
          keep_size: Should keep original size (padding/slicing if needed), default is True.
          kwargs: other arguments for the `np.pad` or `torch.pad` function.
              note that `np.pad` treats channel dimension as the first dimension.
  
      """
  
      backend = [TransformBackends.TORCH]
  
      def __init__(
          self,
          zoom: Union[Sequence[float], float],
          mode: Union[InterpolateMode, str] = InterpolateMode.AREA,
          padding_mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.EDGE,
          align_corners: Optional[bool] = None,
          keep_size: bool = True,
          **kwargs,
      ) -> None:
          self.zoom = zoom
          self.mode: InterpolateMode = InterpolateMode(mode)
          self.padding_mode = padding_mode
          self.align_corners = align_corners
          self.keep_size = keep_size
          self.kwargs = kwargs
  
      def __call__(
          self,
          img: NdarrayOrTensor,
          mode: Optional[Union[InterpolateMode, str]] = None,
          padding_mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None,
          align_corners: Optional[bool] = None,
      ) -> NdarrayOrTensor:
          """
          Args:
              img: channel first array, must have shape: (num_channels, H[, W, ..., ]).
              mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}
                  The interpolation mode. Defaults to ``self.mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
              padding_mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
                  ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
                  available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
                  One of the listed string values or a user supplied function. Defaults to ``"constant"``.
                  The mode to pad data after zooming.
                  See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
                  https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
              align_corners: This only has an effect when mode is
                  'linear', 'bilinear', 'bicubic' or 'trilinear'. Defaults to ``self.align_corners``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
  
          """
          img_t, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float32)
  
          _zoom = ensure_tuple_rep(self.zoom, img.ndim - 1)  # match the spatial image dim
          zoomed: NdarrayOrTensor = torch.nn.functional.interpolate(  # type: ignore
              recompute_scale_factor=True,
              input=img_t.unsqueeze(0),
              scale_factor=list(_zoom),
              mode=look_up_option(self.mode if mode is None else mode, InterpolateMode).value,
              align_corners=self.align_corners if align_corners is None else align_corners,
          )
          zoomed = zoomed.squeeze(0)
  
          if self.keep_size and not np.allclose(img_t.shape, zoomed.shape):
  
              pad_vec = [(0, 0)] * len(img_t.shape)
              slice_vec = [slice(None)] * len(img_t.shape)
              for idx, (od, zd) in enumerate(zip(img_t.shape, zoomed.shape)):
                  diff = od - zd
                  half = abs(diff) // 2
                  if diff > 0:  # need padding
                      pad_vec[idx] = (half, diff - half)
                  elif diff < 0:  # need slicing
                      slice_vec[idx] = slice(half, half + od)
  
              padder = Pad(pad_vec, padding_mode or self.padding_mode)
              zoomed = padder(zoomed)
              zoomed = zoomed[tuple(slice_vec)]
  
          out, *_ = convert_to_dst_type(zoomed, dst=img)
          return out
  
  
  class Rotate90(Transform):
      """
      Rotate an array by 90 degrees in the plane specified by `axes`.
      See np.rot90 for additional details:
      https://numpy.org/doc/stable/reference/generated/numpy.rot90.html.
  
      """
  
      backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
  
      def __init__(self, k: int = 1, spatial_axes: Tuple[int, int] = (0, 1)) -> None:
          """
          Args:
              k: number of times to rotate by 90 degrees.
              spatial_axes: 2 int numbers, defines the plane to rotate with 2 spatial axes.
                  Default: (0, 1), this is the first two axis in spatial dimensions.
                  If axis is negative it counts from the last to the first axis.
          """
          self.k = k
          spatial_axes_: Tuple[int, int] = ensure_tuple(spatial_axes)  # type: ignore
          if len(spatial_axes_) != 2:
              raise ValueError("spatial_axes must be 2 int numbers to indicate the axes to rotate 90 degrees.")
          self.spatial_axes = spatial_axes_
  
      def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
          """
          Args:
              img: channel first array, must have shape: (num_channels, H[, W, ..., ]),
          """
          rot90: Callable = torch.rot90 if isinstance(img, torch.Tensor) else np.rot90  # type: ignore
          out: NdarrayOrTensor = rot90(img, self.k, map_spatial_axes(img.ndim, self.spatial_axes))
          out, *_ = convert_data_type(out, dtype=img.dtype)
          return out
  
  
  class RandRotate90(RandomizableTransform):
      """
      With probability `prob`, input arrays are rotated by 90 degrees
      in the plane specified by `spatial_axes`.
      """
  
      backend = Rotate90.backend
  
      def __init__(self, prob: float = 0.1, max_k: int = 3, spatial_axes: Tuple[int, int] = (0, 1)) -> None:
          """
          Args:
              prob: probability of rotating.
                  (Default 0.1, with 10% probability it returns a rotated array)
              max_k: number of rotations will be sampled from `np.random.randint(max_k) + 1`, (Default 3).
              spatial_axes: 2 int numbers, defines the plane to rotate with 2 spatial axes.
                  Default: (0, 1), this is the first two axis in spatial dimensions.
          """
          RandomizableTransform.__init__(self, prob)
          self.max_k = max_k
          self.spatial_axes = spatial_axes
  
          self._rand_k = 0
  
      def randomize(self, data: Optional[Any] = None) -> None:
          super().randomize(None)
          if not self._do_transform:
              return None
          self._rand_k = self.R.randint(self.max_k) + 1
  
      def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:
          """
          Args:
              img: channel first array, must have shape: (num_channels, H[, W, ..., ]),
              randomize: whether to execute `randomize()` function first, default to True.
          """
          if randomize:
              self.randomize()
  
          if not self._do_transform:
              return img
  
          return Rotate90(self._rand_k, self.spatial_axes)(img)
  
  
  class RandRotate(RandomizableTransform):
      """
      Randomly rotate the input arrays.
  
      Args:
          range_x: Range of rotation angle in radians in the plane defined by the first and second axes.
              If single number, angle is uniformly sampled from (-range_x, range_x).
          range_y: Range of rotation angle in radians in the plane defined by the first and third axes.
              If single number, angle is uniformly sampled from (-range_y, range_y).
          range_z: Range of rotation angle in radians in the plane defined by the second and third axes.
              If single number, angle is uniformly sampled from (-range_z, range_z).
          prob: Probability of rotation.
          keep_size: If it is False, the output shape is adapted so that the
              input array is contained completely in the output.
              If it is True, the output shape is the same as the input. Default is True.
          mode: {``"bilinear"``, ``"nearest"``}
              Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
              See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
          padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
              Padding mode for outside grid values. Defaults to ``"border"``.
              See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
          align_corners: Defaults to False.
              See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
          dtype: data type for resampling computation. Defaults to ``np.float32``.
              If None, use the data type of input data. To be compatible with other modules,
              the output data type is always ``np.float32``.
      """
  
      backend = Rotate.backend
  
      def __init__(
          self,
          range_x: Union[Tuple[float, float], float] = 0.0,
          range_y: Union[Tuple[float, float], float] = 0.0,
          range_z: Union[Tuple[float, float], float] = 0.0,
          prob: float = 0.1,
          keep_size: bool = True,
          mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
          padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
          align_corners: bool = False,
          dtype: Union[DtypeLike, torch.dtype] = np.float32,
      ) -> None:
          RandomizableTransform.__init__(self, prob)
          self.range_x = ensure_tuple(range_x)
          if len(self.range_x) == 1:
              self.range_x = tuple(sorted([-self.range_x[0], self.range_x[0]]))
          self.range_y = ensure_tuple(range_y)
          if len(self.range_y) == 1:
              self.range_y = tuple(sorted([-self.range_y[0], self.range_y[0]]))
          self.range_z = ensure_tuple(range_z)
          if len(self.range_z) == 1:
              self.range_z = tuple(sorted([-self.range_z[0], self.range_z[0]]))
  
          self.keep_size = keep_size
          self.mode: GridSampleMode = look_up_option(mode, GridSampleMode)
          self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode)
          self.align_corners = align_corners
          self.dtype = dtype
  
          self.x = 0.0
          self.y = 0.0
          self.z = 0.0
  
      def randomize(self, data: Optional[Any] = None) -> None:
          super().randomize(None)
          if not self._do_transform:
              return None
          self.x = self.R.uniform(low=self.range_x[0], high=self.range_x[1])
          self.y = self.R.uniform(low=self.range_y[0], high=self.range_y[1])
          self.z = self.R.uniform(low=self.range_z[0], high=self.range_z[1])
  
      def __call__(
          self,
          img: NdarrayOrTensor,
          mode: Optional[Union[GridSampleMode, str]] = None,
          padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
          align_corners: Optional[bool] = None,
          dtype: Union[DtypeLike, torch.dtype] = None,
          randomize: bool = True,
          get_matrix: bool = False,
      ):
          """
          Args:
              img: channel first array, must have shape 2D: (nchannels, H, W), or 3D: (nchannels, H, W, D).
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``self.mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              align_corners: Defaults to ``self.align_corners``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              dtype: data type for resampling computation. Defaults to ``self.dtype``.
                  If None, use the data type of input data. To be compatible with other modules,
                  the output data type is always ``np.float32``.
              randomize: whether to execute `randomize()` function first, default to True.
              get_matrix: whether to return the rotated image and rotate matrix together, default to False.
          """
          if randomize:
              self.randomize()
  
          if not self._do_transform:
              return img
  
          rotator = Rotate(
              angle=self.x if img.ndim == 3 else (self.x, self.y, self.z),
              keep_size=self.keep_size,
              mode=look_up_option(mode or self.mode, GridSampleMode),
              padding_mode=look_up_option(padding_mode or self.padding_mode, GridSamplePadMode),
              align_corners=self.align_corners if align_corners is None else align_corners,
              dtype=dtype or self.dtype or img.dtype,
          )
          img = rotator(img)
          return (img, rotator.get_rotation_matrix()) if get_matrix else img
  
  
  class RandFlip(RandomizableTransform):
      """
      Randomly flips the image along axes. Preserves shape.
      See numpy.flip for additional details.
      https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html
  
      Args:
          prob: Probability of flipping.
          spatial_axis: Spatial axes along which to flip over. Default is None.
      """
  
      backend = Flip.backend
  
      def __init__(self, prob: float = 0.1, spatial_axis: Optional[Union[Sequence[int], int]] = None) -> None:
          RandomizableTransform.__init__(self, prob)
          self.flipper = Flip(spatial_axis=spatial_axis)
  
      def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:
          """
          Args:
              img: channel first array, must have shape: (num_channels, H[, W, ..., ]),
              randomize: whether to execute `randomize()` function first, default to True.
          """
          if randomize:
              self.randomize(None)
  
          if not self._do_transform:
              return img
  
          return self.flipper(img)
  
  
  class RandAxisFlip(RandomizableTransform):
      """
      Randomly select a spatial axis and flip along it.
      See numpy.flip for additional details.
      https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html
  
      Args:
          prob: Probability of flipping.
  
      """
  
      backend = Flip.backend
  
      def __init__(self, prob: float = 0.1) -> None:
          RandomizableTransform.__init__(self, prob)
          self._axis: Optional[int] = None
  
      def randomize(self, data: NdarrayOrTensor) -> None:
          super().randomize(None)
          if not self._do_transform:
              return None
          self._axis = self.R.randint(data.ndim - 1)
  
      def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:
          """
          Args:
              img: channel first array, must have shape: (num_channels, H[, W, ..., ]),
              randomize: whether to execute `randomize()` function first, default to True.
          """
          if randomize:
              self.randomize(data=img)
  
          if not self._do_transform:
              return img
  
          return Flip(spatial_axis=self._axis)(img)
  
  
  class RandZoom(RandomizableTransform):
      """
      Randomly zooms input arrays with given probability within given zoom range.
  
      Args:
          prob: Probability of zooming.
          min_zoom: Min zoom factor. Can be float or sequence same size as image.
              If a float, select a random factor from `[min_zoom, max_zoom]` then apply to all spatial dims
              to keep the original spatial shape ratio.
              If a sequence, min_zoom should contain one value for each spatial axis.
              If 2 values provided for 3D data, use the first value for both H & W dims to keep the same zoom ratio.
          max_zoom: Max zoom factor. Can be float or sequence same size as image.
              If a float, select a random factor from `[min_zoom, max_zoom]` then apply to all spatial dims
              to keep the original spatial shape ratio.
              If a sequence, max_zoom should contain one value for each spatial axis.
              If 2 values provided for 3D data, use the first value for both H & W dims to keep the same zoom ratio.
          mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}
              The interpolation mode. Defaults to ``"area"``.
              See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
          padding_mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
              ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
              available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
              One of the listed string values or a user supplied function. Defaults to ``"constant"``.
              The mode to pad data after zooming.
              See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
              https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
          align_corners: This only has an effect when mode is
              'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None.
              See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
          keep_size: Should keep original size (pad if needed), default is True.
          kwargs: other arguments for the `np.pad` or `torch.pad` function.
              note that `np.pad` treats channel dimension as the first dimension.
  
      """
  
      backend = Zoom.backend
  
      def __init__(
          self,
          prob: float = 0.1,
          min_zoom: Union[Sequence[float], float] = 0.9,
          max_zoom: Union[Sequence[float], float] = 1.1,
          mode: Union[InterpolateMode, str] = InterpolateMode.AREA,
          padding_mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.EDGE,
          align_corners: Optional[bool] = None,
          keep_size: bool = True,
          **kwargs,
      ) -> None:
          RandomizableTransform.__init__(self, prob)
          self.min_zoom = ensure_tuple(min_zoom)
          self.max_zoom = ensure_tuple(max_zoom)
          if len(self.min_zoom) != len(self.max_zoom):
              raise AssertionError("min_zoom and max_zoom must have same length.")
          self.mode: InterpolateMode = look_up_option(mode, InterpolateMode)
          self.padding_mode = padding_mode
          self.align_corners = align_corners
          self.keep_size = keep_size
          self.kwargs = kwargs
  
          self._zoom: Sequence[float] = [1.0]
  
      def randomize(self, img: NdarrayOrTensor) -> None:
          super().randomize(None)
          if not self._do_transform:
              return None
          self._zoom = [self.R.uniform(l, h) for l, h in zip(self.min_zoom, self.max_zoom)]
          if len(self._zoom) == 1:
              # to keep the spatial shape ratio, use same random zoom factor for all dims
              self._zoom = ensure_tuple_rep(self._zoom[0], img.ndim - 1)
          elif len(self._zoom) == 2 and img.ndim > 3:
              # if 2 zoom factors provided for 3D data, use the first factor for H and W dims, second factor for D dim
              self._zoom = ensure_tuple_rep(self._zoom[0], img.ndim - 2) + ensure_tuple(self._zoom[-1])
  
      def __call__(
          self,
          img: NdarrayOrTensor,
          mode: Optional[Union[InterpolateMode, str]] = None,
          padding_mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None,
          align_corners: Optional[bool] = None,
          randomize: bool = True,
      ) -> NdarrayOrTensor:
          """
          Args:
              img: channel first array, must have shape 2D: (nchannels, H, W), or 3D: (nchannels, H, W, D).
              mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}
                  The interpolation mode. Defaults to ``self.mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
              padding_mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
                  ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
                  available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
                  One of the listed string values or a user supplied function. Defaults to ``"constant"``.
                  The mode to pad data after zooming.
                  See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
                  https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
              align_corners: This only has an effect when mode is
                  'linear', 'bilinear', 'bicubic' or 'trilinear'. Defaults to ``self.align_corners``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
              randomize: whether to execute `randomize()` function first, default to True.
  
          """
          # match the spatial image dim
          if randomize:
              self.randomize(img=img)
  
          if not self._do_transform:
              return img
  
          return Zoom(
              self._zoom,
              keep_size=self.keep_size,
              mode=look_up_option(mode or self.mode, InterpolateMode),
              padding_mode=padding_mode or self.padding_mode,
              align_corners=align_corners or self.align_corners,
              **self.kwargs,
          )(img)
  
  
  class AffineGrid(Transform):
      """
      Affine transforms on the coordinates.
  
      Args:
          rotate_params: a rotation angle in radians, a scalar for 2D image, a tuple of 3 floats for 3D.
              Defaults to no rotation.
          shear_params: shearing factors for affine matrix, take a 3D affine as example::
  
              [
                  [1.0, params[0], params[1], 0.0],
                  [params[2], 1.0, params[3], 0.0],
                  [params[4], params[5], 1.0, 0.0],
                  [0.0, 0.0, 0.0, 1.0],
              ]
  
              a tuple of 2 floats for 2D, a tuple of 6 floats for 3D. Defaults to no shearing.
          translate_params: a tuple of 2 floats for 2D, a tuple of 3 floats for 3D. Translation is in
              pixel/voxel relative to the center of the input image. Defaults to no translation.
          scale_params: scale factor for every spatial dims. a tuple of 2 floats for 2D,
              a tuple of 3 floats for 3D. Defaults to `1.0`.
          dtype: data type for the grid computation. Defaults to ``np.float32``.
              If ``None``, use the data type of input data (if `grid` is provided).
          device: device on which the tensor will be allocated, if a new grid is generated.
          affine: If applied, ignore the params (`rotate_params`, etc.) and use the
              supplied matrix. Should be square with each side = num of image spatial
              dimensions + 1.
  
      .. deprecated:: 0.6.0
          ``as_tensor_output`` is deprecated.
  
      """
  
      backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
  
      @deprecated_arg(name="as_tensor_output", since="0.6")
      def __init__(
          self,
          rotate_params: Optional[Union[Sequence[float], float]] = None,
          shear_params: Optional[Union[Sequence[float], float]] = None,
          translate_params: Optional[Union[Sequence[float], float]] = None,
          scale_params: Optional[Union[Sequence[float], float]] = None,
          as_tensor_output: bool = True,
          device: Optional[torch.device] = None,
          dtype: DtypeLike = np.float32,
          affine: Optional[NdarrayOrTensor] = None,
      ) -> None:
          self.rotate_params = rotate_params
          self.shear_params = shear_params
          self.translate_params = translate_params
          self.scale_params = scale_params
          self.device = device
          self.dtype = dtype
          self.affine = affine
  
      def __call__(
          self, spatial_size: Optional[Sequence[int]] = None, grid: Optional[NdarrayOrTensor] = None
      ) -> Tuple[NdarrayOrTensor, NdarrayOrTensor]:
          """
          The grid can be initialized with a `spatial_size` parameter, or provided directly as `grid`.
          Therefore, either `spatial_size` or `grid` must be provided.
          When initialising from `spatial_size`, the backend "torch" will be used.
  
          Args:
              spatial_size: output grid size.
              grid: grid to be transformed. Shape must be (3, H, W) for 2D or (4, H, W, D) for 3D.
  
          Raises:
              ValueError: When ``grid=None`` and ``spatial_size=None``. Incompatible values.
  
          """
          if grid is None:  # create grid from spatial_size
              if spatial_size is None:
                  raise ValueError("Incompatible values: grid=None and spatial_size=None.")
              grid = create_grid(spatial_size, device=self.device, backend="torch", dtype=self.dtype)
          _b = TransformBackends.TORCH if isinstance(grid, torch.Tensor) else TransformBackends.NUMPY
          _device = grid.device if isinstance(grid, torch.Tensor) else self.device
          affine: NdarrayOrTensor
          if self.affine is None:
              spatial_dims = len(grid.shape) - 1
              affine = (
                  torch.eye(spatial_dims + 1, device=_device)
                  if _b == TransformBackends.TORCH
                  else np.eye(spatial_dims + 1)
              )
              if self.rotate_params:
                  affine = affine @ create_rotate(spatial_dims, self.rotate_params, device=_device, backend=_b)
              if self.shear_params:
                  affine = affine @ create_shear(spatial_dims, self.shear_params, device=_device, backend=_b)
              if self.translate_params:
                  affine = affine @ create_translate(spatial_dims, self.translate_params, device=_device, backend=_b)
              if self.scale_params:
                  affine = affine @ create_scale(spatial_dims, self.scale_params, device=_device, backend=_b)
          else:
              affine = self.affine
  
          grid, *_ = convert_data_type(grid, torch.Tensor, device=_device, dtype=self.dtype or grid.dtype)
          affine, *_ = convert_to_dst_type(affine, grid)
  
          grid = (affine @ grid.reshape((grid.shape[0], -1))).reshape([-1] + list(grid.shape[1:]))
          return grid, affine
  
  
  class RandAffineGrid(Randomizable, Transform):
      """
      Generate randomised affine grid.
  
      """
  
      backend = AffineGrid.backend
  
      @deprecated_arg(name="as_tensor_output", since="0.6")
      def __init__(
          self,
          rotate_range: RandRange = None,
          shear_range: RandRange = None,
          translate_range: RandRange = None,
          scale_range: RandRange = None,
          as_tensor_output: bool = True,
          device: Optional[torch.device] = None,
      ) -> None:
          """
          Args:
              rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then
                  `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter
                  for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used.
                  This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be
                  in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]`
                  for dim0 and nothing for the remaining dimensions.
              shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select
                  shearing factors(a tuple of 2 floats for 2D, a tuple of 6 floats for 3D) for affine matrix,
                  take a 3D affine as example::
  
                      [
                          [1.0, params[0], params[1], 0.0],
                          [params[2], 1.0, params[3], 0.0],
                          [params[4], params[5], 1.0, 0.0],
                          [0.0, 0.0, 0.0, 1.0],
                      ]
  
              translate_range: translate range with format matching `rotate_range`, it defines the range to randomly
                  select voxels to translate for every spatial dims.
              scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select
                  the scale factor to translate for every spatial dims. A value of 1.0 is added to the result.
                  This allows 0 to correspond to no change (i.e., a scaling of 1.0).
              device: device to store the output grid data.
  
          See also:
              - :py:meth:`monai.transforms.utils.create_rotate`
              - :py:meth:`monai.transforms.utils.create_shear`
              - :py:meth:`monai.transforms.utils.create_translate`
              - :py:meth:`monai.transforms.utils.create_scale`
  
          .. deprecated:: 0.6.0
              ``as_tensor_output`` is deprecated.
  
          """
          self.rotate_range = ensure_tuple(rotate_range)
          self.shear_range = ensure_tuple(shear_range)
          self.translate_range = ensure_tuple(translate_range)
          self.scale_range = ensure_tuple(scale_range)
  
          self.rotate_params: Optional[List[float]] = None
          self.shear_params: Optional[List[float]] = None
          self.translate_params: Optional[List[float]] = None
          self.scale_params: Optional[List[float]] = None
  
          self.device = device
          self.affine: Optional[NdarrayOrTensor] = None
  
      def _get_rand_param(self, param_range, add_scalar: float = 0.0):
          out_param = []
          for f in param_range:
              if issequenceiterable(f):
                  if len(f) != 2:
                      raise ValueError("If giving range as [min,max], should only have two elements per dim.")
                  out_param.append(self.R.uniform(f[0], f[1]) + add_scalar)
              elif f is not None:
                  out_param.append(self.R.uniform(-f, f) + add_scalar)
          return out_param
  
      def randomize(self, data: Optional[Any] = None) -> None:
          self.rotate_params = self._get_rand_param(self.rotate_range)
          self.shear_params = self._get_rand_param(self.shear_range)
          self.translate_params = self._get_rand_param(self.translate_range)
          self.scale_params = self._get_rand_param(self.scale_range, 1.0)
  
      def __call__(
          self, spatial_size: Optional[Sequence[int]] = None, grid: Optional[NdarrayOrTensor] = None
      ) -> NdarrayOrTensor:
          """
          Args:
              spatial_size: output grid size.
              grid: grid to be transformed. Shape must be (3, H, W) for 2D or (4, H, W, D) for 3D.
  
          Returns:
              a 2D (3xHxW) or 3D (4xHxWxD) grid.
          """
          self.randomize()
          affine_grid = AffineGrid(
              rotate_params=self.rotate_params,
              shear_params=self.shear_params,
              translate_params=self.translate_params,
              scale_params=self.scale_params,
              device=self.device,
          )
          _grid: NdarrayOrTensor
          _grid, self.affine = affine_grid(spatial_size, grid)
          return _grid
  
      def get_transformation_matrix(self) -> Optional[NdarrayOrTensor]:
          """Get the most recently applied transformation matrix"""
          return self.affine
  
  
  class RandDeformGrid(Randomizable, Transform):
      """
      Generate random deformation grid.
      """
  
      backend = [TransformBackends.TORCH]
  
      def __init__(
          self,
          spacing: Union[Sequence[float], float],
          magnitude_range: Tuple[float, float],
          as_tensor_output: bool = True,
          device: Optional[torch.device] = None,
      ) -> None:
          """
          Args:
              spacing: spacing of the grid in 2D or 3D.
                  e.g., spacing=(1, 1) indicates pixel-wise deformation in 2D,
                  spacing=(1, 1, 1) indicates voxel-wise deformation in 3D,
                  spacing=(2, 2) indicates deformation field defined on every other pixel in 2D.
              magnitude_range: the random offsets will be generated from
                  `uniform[magnitude[0], magnitude[1])`.
              as_tensor_output: whether to output tensor instead of numpy array.
                  defaults to True.
              device: device to store the output grid data.
          """
          self.spacing = spacing
          self.magnitude = magnitude_range
  
          self.rand_mag = 1.0
          self.as_tensor_output = as_tensor_output
          self.random_offset: np.ndarray
          self.device = device
  
      def randomize(self, grid_size: Sequence[int]) -> None:
          self.random_offset = self.R.normal(size=([len(grid_size)] + list(grid_size))).astype(np.float32, copy=False)
          self.rand_mag = self.R.uniform(self.magnitude[0], self.magnitude[1])
  
      def __call__(self, spatial_size: Sequence[int]):
          """
          Args:
              spatial_size: spatial size of the grid.
          """
          self.spacing = fall_back_tuple(self.spacing, (1.0,) * len(spatial_size))
          control_grid = create_control_grid(spatial_size, self.spacing, device=self.device, backend="torch")
          self.randomize(control_grid.shape[1:])
          _offset, *_ = convert_to_dst_type(self.rand_mag * self.random_offset, control_grid)
          control_grid[: len(spatial_size)] += _offset
          if not self.as_tensor_output:
              control_grid, *_ = convert_data_type(control_grid, output_type=np.ndarray, dtype=np.float32)
          return control_grid
  
  
  class Resample(Transform):
  
      backend = [TransformBackends.TORCH]
  
      @deprecated_arg(name="as_tensor_output", since="0.6")
      def __init__(
          self,
          mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
          padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
          as_tensor_output: bool = True,
          norm_coords: bool = True,
          device: Optional[torch.device] = None,
          dtype: DtypeLike = np.float64,
      ) -> None:
          """
          computes output image using values from `img`, locations from `grid` using pytorch.
          supports spatially 2D or 3D (num_channels, H, W[, D]).
  
          Args:
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``"border"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
                  When `USE_COMPILED` is `True`, this argument uses
                  ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations.
                  See also: https://docs.monai.io/en/stable/networks.html#grid-pull
              norm_coords: whether to normalize the coordinates from `[-(size-1)/2, (size-1)/2]` to
                  `[0, size - 1]` (for ``monai/csrc`` implementation) or
                  `[-1, 1]` (for torch ``grid_sample`` implementation) to be compatible with the underlying
                  resampling API.
              device: device on which the tensor will be allocated.
              dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.
                  If ``None``, use the data type of input data. To be compatible with other modules,
                  the output data type is always `float32`.
  
          .. deprecated:: 0.6.0
              ``as_tensor_output`` is deprecated.
  
          """
          self.mode: GridSampleMode = look_up_option(mode, GridSampleMode)
          self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode)
          self.norm_coords = norm_coords
          self.device = device
          self.dtype = dtype
  
      def __call__(
          self,
          img: NdarrayOrTensor,
          grid: Optional[NdarrayOrTensor] = None,
          mode: Optional[Union[GridSampleMode, str]] = None,
          padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
          dtype: DtypeLike = None,
      ) -> NdarrayOrTensor:
          """
          Args:
              img: shape must be (num_channels, H, W[, D]).
              grid: shape must be (3, H, W) for 2D or (4, H, W, D) for 3D.
                  if ``norm_coords`` is True, the grid values must be in `[-(size-1)/2, (size-1)/2]`.
                  if ``USE_COMPILED=True`` and ``norm_coords=False``, grid values must be in `[0, size-1]`.
                  if ``USE_COMPILED=False`` and ``norm_coords=False``, grid values must be in `[-1, 1]`.
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``self.mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
                  When `USE_COMPILED` is `True`, this argument uses
                  ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations.
                  See also: https://docs.monai.io/en/stable/networks.html#grid-pull
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              dtype: data type for resampling computation. Defaults to ``self.dtype``.
                  To be compatible with other modules, the output data type is always `float32`.
  
          See also:
              :py:const:`monai.config.USE_COMPILED`
          """
          if grid is None:
              raise ValueError("Unknown grid.")
          _device = img.device if isinstance(img, torch.Tensor) else self.device
          _dtype = dtype or self.dtype or img.dtype
          img_t, *_ = convert_data_type(img, torch.Tensor, device=_device, dtype=_dtype)
          grid_t = convert_to_dst_type(grid, img_t)[0]
          if grid_t is grid:  # copy if needed (convert_data_type converts to contiguous)
              grid_t = grid_t.clone(memory_format=torch.contiguous_format)
          sr = min(len(img_t.shape[1:]), 3)
  
          if USE_COMPILED:
              if self.norm_coords:
                  for i, dim in enumerate(img_t.shape[1 : 1 + sr]):
                      grid_t[i] = (max(dim, 2) / 2.0 - 0.5 + grid_t[i]) / grid_t[-1:]
              grid_t = moveaxis(grid_t[:sr], 0, -1)  # type: ignore
              _padding_mode = self.padding_mode if padding_mode is None else padding_mode
              _padding_mode = _padding_mode.value if isinstance(_padding_mode, GridSamplePadMode) else _padding_mode
              bound = 1 if _padding_mode == "reflection" else _padding_mode
              _interp_mode = self.mode if mode is None else mode
              _interp_mode = _interp_mode.value if isinstance(_interp_mode, GridSampleMode) else _interp_mode
              if _interp_mode == "bicubic":
                  interp = 3
              elif _interp_mode == "bilinear":
                  interp = 1
              else:
                  interp = _interp_mode  # type: ignore
              out = grid_pull(
                  img_t.unsqueeze(0), grid_t.unsqueeze(0), bound=bound, extrapolate=True, interpolation=interp
              )[0]
          else:
              if self.norm_coords:
                  for i, dim in enumerate(img_t.shape[1 : 1 + sr]):
                      grid_t[i] = 2.0 / (max(2, dim) - 1.0) * grid_t[i] / grid_t[-1:]
              index_ordering: List[int] = list(range(sr - 1, -1, -1))
              grid_t = moveaxis(grid_t[index_ordering], 0, -1)  # type: ignore
              out = torch.nn.functional.grid_sample(
                  img_t.unsqueeze(0),
                  grid_t.unsqueeze(0),
                  mode=self.mode.value if mode is None else GridSampleMode(mode).value,
                  padding_mode=self.padding_mode.value if padding_mode is None else GridSamplePadMode(padding_mode).value,
                  align_corners=True,
              )[0]
          out_val, *_ = convert_to_dst_type(out, dst=img, dtype=np.float32)
          return out_val
  
  
  class Affine(Transform):
      """
      Transform ``img`` given the affine parameters.
      A tutorial is available: https://github.com/Project-MONAI/tutorials/blob/0.6.0/modules/transforms_demo_2d.ipynb.
  
      """
  
      backend = list(set(AffineGrid.backend) & set(Resample.backend))
  
      @deprecated_arg(name="as_tensor_output", since="0.6")
      def __init__(
          self,
          rotate_params: Optional[Union[Sequence[float], float]] = None,
          shear_params: Optional[Union[Sequence[float], float]] = None,
          translate_params: Optional[Union[Sequence[float], float]] = None,
          scale_params: Optional[Union[Sequence[float], float]] = None,
          affine: Optional[NdarrayOrTensor] = None,
          spatial_size: Optional[Union[Sequence[int], int]] = None,
          mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
          padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,
          norm_coords: bool = True,
          as_tensor_output: bool = True,
          device: Optional[torch.device] = None,
          dtype: DtypeLike = np.float32,
          image_only: bool = False,
      ) -> None:
          """
          The affine transformations are applied in rotate, shear, translate, scale order.
  
          Args:
              rotate_params: a rotation angle in radians, a scalar for 2D image, a tuple of 3 floats for 3D.
                  Defaults to no rotation.
              shear_params: shearing factors for affine matrix, take a 3D affine as example::
  
                  [
                      [1.0, params[0], params[1], 0.0],
                      [params[2], 1.0, params[3], 0.0],
                      [params[4], params[5], 1.0, 0.0],
                      [0.0, 0.0, 0.0, 1.0],
                  ]
  
                  a tuple of 2 floats for 2D, a tuple of 6 floats for 3D. Defaults to no shearing.
              translate_params: a tuple of 2 floats for 2D, a tuple of 3 floats for 3D. Translation is in
                  pixel/voxel relative to the center of the input image. Defaults to no translation.
              scale_params: scale factor for every spatial dims. a tuple of 2 floats for 2D,
                  a tuple of 3 floats for 3D. Defaults to `1.0`.
              affine: If applied, ignore the params (`rotate_params`, etc.) and use the
                  supplied matrix. Should be square with each side = num of image spatial
                  dimensions + 1.
              spatial_size: output image spatial size.
                  if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                  the transform will use the spatial size of `img`.
                  if some components of the `spatial_size` are non-positive values, the transform will use the
                  corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted
                  to `(32, 64)` if the second spatial dimension size of img is `64`.
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
                  When `USE_COMPILED` is `True`, this argument uses
                  ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations.
                  See also: https://docs.monai.io/en/stable/networks.html#grid-pull
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``"reflection"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              norm_coords: whether to normalize the coordinates from `[-(size-1)/2, (size-1)/2]` to
                  `[0, size - 1]` or `[-1, 1]` to be compatible with the underlying resampling API.
                  If the coordinates are generated by ``monai.transforms.utils.create_grid``
                  and the ``affine`` doesn't include the normalization, this argument should be set to ``True``.
                  If the output `self.affine_grid` is already normalized, this argument should be set to ``False``.
              device: device on which the tensor will be allocated.
              dtype: data type for resampling computation. Defaults to ``np.float32``.
                  If ``None``, use the data type of input data. To be compatible with other modules,
                  the output data type is always `float32`.
              image_only: if True return only the image volume, otherwise return (image, affine).
  
          .. deprecated:: 0.6.0
              ``as_tensor_output`` is deprecated.
  
          """
          self.affine_grid = AffineGrid(
              rotate_params=rotate_params,
              shear_params=shear_params,
              translate_params=translate_params,
              scale_params=scale_params,
              affine=affine,
              dtype=dtype,
              device=device,
          )
          self.image_only = image_only
          self.resampler = Resample(norm_coords=norm_coords, device=device, dtype=dtype)
          self.spatial_size = spatial_size
          self.mode: GridSampleMode = look_up_option(mode, GridSampleMode)
          self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode)
  
      def __call__(
          self,
          img: NdarrayOrTensor,
          spatial_size: Optional[Union[Sequence[int], int]] = None,
          mode: Optional[Union[GridSampleMode, str]] = None,
          padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
      ) -> Union[NdarrayOrTensor, Tuple[NdarrayOrTensor, NdarrayOrTensor]]:
          """
          Args:
              img: shape must be (num_channels, H, W[, D]),
              spatial_size: output image spatial size.
                  if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                  the transform will use the spatial size of `img`.
                  if `img` has two spatial dimensions, `spatial_size` should have 2 elements [h, w].
                  if `img` has three spatial dimensions, `spatial_size` should have 3 elements [h, w, d].
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``self.mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
                  When `USE_COMPILED` is `True`, this argument uses
                  ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations.
                  See also: https://docs.monai.io/en/stable/networks.html#grid-pull
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
          """
          sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:])
          grid, affine = self.affine_grid(spatial_size=sp_size)
          ret = self.resampler(img, grid=grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode)
  
          return ret if self.image_only else (ret, affine)
  
  
  class RandAffine(RandomizableTransform):
      """
      Random affine transform.
      A tutorial is available: https://github.com/Project-MONAI/tutorials/blob/0.6.0/modules/transforms_demo_2d.ipynb.
  
      """
  
      backend = Affine.backend
  
      @deprecated_arg(name="as_tensor_output", since="0.6")
      def __init__(
          self,
          prob: float = 0.1,
          rotate_range: RandRange = None,
          shear_range: RandRange = None,
          translate_range: RandRange = None,
          scale_range: RandRange = None,
          spatial_size: Optional[Union[Sequence[int], int]] = None,
          mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
          padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,
          cache_grid: bool = False,
          as_tensor_output: bool = True,
          device: Optional[torch.device] = None,
      ) -> None:
          """
          Args:
              prob: probability of returning a randomized affine grid.
                  defaults to 0.1, with 10% chance returns a randomized grid.
              rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then
                  `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter
                  for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used.
                  This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be
                  in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]`
                  for dim0 and nothing for the remaining dimensions.
              shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select
                  shearing factors(a tuple of 2 floats for 2D, a tuple of 6 floats for 3D) for affine matrix,
                  take a 3D affine as example::
  
                      [
                          [1.0, params[0], params[1], 0.0],
                          [params[2], 1.0, params[3], 0.0],
                          [params[4], params[5], 1.0, 0.0],
                          [0.0, 0.0, 0.0, 1.0],
                      ]
  
              translate_range: translate range with format matching `rotate_range`, it defines the range to randomly
                  select pixel/voxel to translate for every spatial dims.
              scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select
                  the scale factor to translate for every spatial dims. A value of 1.0 is added to the result.
                  This allows 0 to correspond to no change (i.e., a scaling of 1.0).
              spatial_size: output image spatial size.
                  if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                  the transform will use the spatial size of `img`.
                  if some components of the `spatial_size` are non-positive values, the transform will use the
                  corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted
                  to `(32, 64)` if the second spatial dimension size of img is `64`.
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``"reflection"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              cache_grid: whether to cache the identity sampling grid.
                  If the spatial size is not dynamically defined by input image, enabling this option could
                  accelerate the transform.
              device: device on which the tensor will be allocated.
  
          See also:
              - :py:class:`RandAffineGrid` for the random affine parameters configurations.
              - :py:class:`Affine` for the affine transformation parameters configurations.
  
          .. deprecated:: 0.6.0
              ``as_tensor_output`` is deprecated.
  
          """
          RandomizableTransform.__init__(self, prob)
  
          self.rand_affine_grid = RandAffineGrid(
              rotate_range=rotate_range,
              shear_range=shear_range,
              translate_range=translate_range,
              scale_range=scale_range,
              device=device,
          )
          self.resampler = Resample(device=device)
  
          self.spatial_size = spatial_size
          self.cache_grid = cache_grid
          self._cached_grid = self._init_identity_cache()
          self.mode: GridSampleMode = GridSampleMode(mode)
          self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
  
      def _init_identity_cache(self):
          """
          Create cache of the identity grid if cache_grid=True and spatial_size is known.
          """
          if self.spatial_size is None:
              if self.cache_grid:
                  warnings.warn(
                      "cache_grid=True is not compatible with the dynamic spatial_size, please specify 'spatial_size'."
                  )
              return None
          _sp_size = ensure_tuple(self.spatial_size)
          _ndim = len(_sp_size)
          if _sp_size != fall_back_tuple(_sp_size, [1] * _ndim) or _sp_size != fall_back_tuple(_sp_size, [2] * _ndim):
              # dynamic shape because it falls back to different outcomes
              if self.cache_grid:
                  warnings.warn(
                      "cache_grid=True is not compatible with the dynamic spatial_size "
                      f"'spatial_size={self.spatial_size}', please specify 'spatial_size'."
                  )
              return None
          return create_grid(spatial_size=_sp_size, device=self.rand_affine_grid.device, backend="torch")
  
      def get_identity_grid(self, spatial_size: Sequence[int]):
          """
          Return a cached or new identity grid depends on the availability.
  
          Args:
              spatial_size: non-dynamic spatial size
          """
          ndim = len(spatial_size)
          if spatial_size != fall_back_tuple(spatial_size, [1] * ndim) or spatial_size != fall_back_tuple(
              spatial_size, [2] * ndim
          ):
              raise RuntimeError(f"spatial_size should not be dynamic, got {spatial_size}.")
          return (
              create_grid(spatial_size=spatial_size, device=self.rand_affine_grid.device, backend="torch")
              if self._cached_grid is None
              else self._cached_grid
          )
  
      def set_random_state(
          self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None
      ) -> "RandAffine":
          self.rand_affine_grid.set_random_state(seed, state)
          super().set_random_state(seed, state)
          return self
  
      def randomize(self, data: Optional[Any] = None) -> None:
          super().randomize(None)
          if not self._do_transform:
              return None
          self.rand_affine_grid.randomize()
  
      def __call__(
          self,
          img: NdarrayOrTensor,
          spatial_size: Optional[Union[Sequence[int], int]] = None,
          mode: Optional[Union[GridSampleMode, str]] = None,
          padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
          randomize: bool = True,
      ) -> NdarrayOrTensor:
          """
          Args:
              img: shape must be (num_channels, H, W[, D]),
              spatial_size: output image spatial size.
                  if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                  the transform will use the spatial size of `img`.
                  if `img` has two spatial dimensions, `spatial_size` should have 2 elements [h, w].
                  if `img` has three spatial dimensions, `spatial_size` should have 3 elements [h, w, d].
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``self.mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              randomize: whether to execute `randomize()` function first, default to True.
  
          """
          if randomize:
              self.randomize()
  
          # if not doing transform and spatial size doesn't change, nothing to do
          # except convert to float and device
          sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:])
          do_resampling = self._do_transform or (sp_size != ensure_tuple(img.shape[1:]))
          if not do_resampling:
              img, *_ = convert_data_type(img, dtype=torch.float32, device=self.resampler.device)
          grid = self.get_identity_grid(sp_size)
          if self._do_transform:
              grid = self.rand_affine_grid(grid=grid)
          out: NdarrayOrTensor = self.resampler(
              img=img, grid=grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode
          )
          return out
  
  
  class Rand2DElastic(RandomizableTransform):
      """
      Random elastic deformation and affine in 2D.
      A tutorial is available: https://github.com/Project-MONAI/tutorials/blob/0.6.0/modules/transforms_demo_2d.ipynb.
  
      """
  
      backend = Resample.backend
  
      @deprecated_arg(name="as_tensor_output", since="0.6")
      def __init__(
          self,
          spacing: Union[Tuple[float, float], float],
          magnitude_range: Tuple[float, float],
          prob: float = 0.1,
          rotate_range: RandRange = None,
          shear_range: RandRange = None,
          translate_range: RandRange = None,
          scale_range: RandRange = None,
          spatial_size: Optional[Union[Tuple[int, int], int]] = None,
          mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
          padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,
          as_tensor_output: bool = False,
          device: Optional[torch.device] = None,
      ) -> None:
          """
          Args:
              spacing : distance in between the control points.
              magnitude_range: the random offsets will be generated from ``uniform[magnitude[0], magnitude[1])``.
              prob: probability of returning a randomized elastic transform.
                  defaults to 0.1, with 10% chance returns a randomized elastic transform,
                  otherwise returns a ``spatial_size`` centered area extracted from the input image.
              rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then
                  `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter
                  for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used.
                  This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be
                  in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]`
                  for dim0 and nothing for the remaining dimensions.
              shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select
                  shearing factors(a tuple of 2 floats for 2D) for affine matrix, take a 2D affine as example::
  
                      [
                          [1.0, params[0], 0.0],
                          [params[1], 1.0, 0.0],
                          [0.0, 0.0, 1.0],
                      ]
  
              translate_range: translate range with format matching `rotate_range`, it defines the range to randomly
                  select pixel to translate for every spatial dims.
              scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select
                  the scale factor to translate for every spatial dims. A value of 1.0 is added to the result.
                  This allows 0 to correspond to no change (i.e., a scaling of 1.0).
              spatial_size: specifying output image spatial size [h, w].
                  if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                  the transform will use the spatial size of `img`.
                  if some components of the `spatial_size` are non-positive values, the transform will use the
                  corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted
                  to `(32, 64)` if the second spatial dimension size of img is `64`.
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``"reflection"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              device: device on which the tensor will be allocated.
  
          See also:
              - :py:class:`RandAffineGrid` for the random affine parameters configurations.
              - :py:class:`Affine` for the affine transformation parameters configurations.
  
          .. deprecated:: 0.6.0
              ``as_tensor_output`` is deprecated.
  
          """
          RandomizableTransform.__init__(self, prob)
          self.deform_grid = RandDeformGrid(
              spacing=spacing, magnitude_range=magnitude_range, as_tensor_output=True, device=device
          )
          self.rand_affine_grid = RandAffineGrid(
              rotate_range=rotate_range,
              shear_range=shear_range,
              translate_range=translate_range,
              scale_range=scale_range,
              device=device,
          )
          self.resampler = Resample(device=device)
  
          self.device = device
          self.spatial_size = spatial_size
          self.mode: GridSampleMode = look_up_option(mode, GridSampleMode)
          self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode)
  
      def set_random_state(
          self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None
      ) -> "Rand2DElastic":
          self.deform_grid.set_random_state(seed, state)
          self.rand_affine_grid.set_random_state(seed, state)
          super().set_random_state(seed, state)
          return self
  
      def randomize(self, spatial_size: Sequence[int]) -> None:
          super().randomize(None)
          if not self._do_transform:
              return None
          self.deform_grid.randomize(spatial_size)
          self.rand_affine_grid.randomize()
  
      def __call__(
          self,
          img: NdarrayOrTensor,
          spatial_size: Optional[Union[Tuple[int, int], int]] = None,
          mode: Optional[Union[GridSampleMode, str]] = None,
          padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
          randomize: bool = True,
      ) -> NdarrayOrTensor:
          """
          Args:
              img: shape must be (num_channels, H, W),
              spatial_size: specifying output image spatial size [h, w].
                  if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                  the transform will use the spatial size of `img`.
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``self.mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              randomize: whether to execute `randomize()` function first, default to True.
          """
          sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:])
          if randomize:
              self.randomize(spatial_size=sp_size)
  
          if self._do_transform:
              grid = self.deform_grid(spatial_size=sp_size)
              grid = self.rand_affine_grid(grid=grid)
              grid = torch.nn.functional.interpolate(  # type: ignore
                  recompute_scale_factor=True,
                  input=grid.unsqueeze(0),
                  scale_factor=list(ensure_tuple(self.deform_grid.spacing)),
                  mode=InterpolateMode.BICUBIC.value,
                  align_corners=False,
              )
              grid = CenterSpatialCrop(roi_size=sp_size)(grid[0])
          else:
              _device = img.device if isinstance(img, torch.Tensor) else self.device
              grid = create_grid(spatial_size=sp_size, device=_device, backend="torch")
          out: NdarrayOrTensor = self.resampler(
              img, grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode
          )
          return out
  
  
  class Rand3DElastic(RandomizableTransform):
      """
      Random elastic deformation and affine in 3D.
      A tutorial is available: https://github.com/Project-MONAI/tutorials/blob/0.6.0/modules/transforms_demo_2d.ipynb.
  
      """
  
      backend = Resample.backend
  
      @deprecated_arg(name="as_tensor_output", since="0.6")
      def __init__(
          self,
          sigma_range: Tuple[float, float],
          magnitude_range: Tuple[float, float],
          prob: float = 0.1,
          rotate_range: RandRange = None,
          shear_range: RandRange = None,
          translate_range: RandRange = None,
          scale_range: RandRange = None,
          spatial_size: Optional[Union[Tuple[int, int, int], int]] = None,
          mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
          padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,
          as_tensor_output: bool = False,
          device: Optional[torch.device] = None,
      ) -> None:
          """
          Args:
              sigma_range: a Gaussian kernel with standard deviation sampled from
                  ``uniform[sigma_range[0], sigma_range[1])`` will be used to smooth the random offset grid.
              magnitude_range: the random offsets on the grid will be generated from
                  ``uniform[magnitude[0], magnitude[1])``.
              prob: probability of returning a randomized elastic transform.
                  defaults to 0.1, with 10% chance returns a randomized elastic transform,
                  otherwise returns a ``spatial_size`` centered area extracted from the input image.
              rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then
                  `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter
                  for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used.
                  This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be
                  in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]`
                  for dim0 and nothing for the remaining dimensions.
              shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select
                  shearing factors(a tuple of 6 floats for 3D) for affine matrix, take a 3D affine as example::
  
                      [
                          [1.0, params[0], params[1], 0.0],
                          [params[2], 1.0, params[3], 0.0],
                          [params[4], params[5], 1.0, 0.0],
                          [0.0, 0.0, 0.0, 1.0],
                      ]
  
              translate_range: translate range with format matching `rotate_range`, it defines the range to randomly
                  select voxel to translate for every spatial dims.
              scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select
                  the scale factor to translate for every spatial dims. A value of 1.0 is added to the result.
                  This allows 0 to correspond to no change (i.e., a scaling of 1.0).
              spatial_size: specifying output image spatial size [h, w, d].
                  if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                  the transform will use the spatial size of `img`.
                  if some components of the `spatial_size` are non-positive values, the transform will use the
                  corresponding components of img size. For example, `spatial_size=(32, 32, -1)` will be adapted
                  to `(32, 32, 64)` if the third spatial dimension size of img is `64`.
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``"reflection"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              device: device on which the tensor will be allocated.
  
          See also:
              - :py:class:`RandAffineGrid` for the random affine parameters configurations.
              - :py:class:`Affine` for the affine transformation parameters configurations.
  
          .. deprecated:: 0.6.0
              ``as_tensor_output`` is deprecated.
  
          """
          RandomizableTransform.__init__(self, prob)
          self.rand_affine_grid = RandAffineGrid(
              rotate_range=rotate_range,
              shear_range=shear_range,
              translate_range=translate_range,
              scale_range=scale_range,
              device=device,
          )
          self.resampler = Resample(device=device)
  
          self.sigma_range = sigma_range
          self.magnitude_range = magnitude_range
          self.spatial_size = spatial_size
          self.mode: GridSampleMode = look_up_option(mode, GridSampleMode)
          self.padding_mode: GridSamplePadMode = look_up_option(padding_mode, GridSamplePadMode)
          self.device = device
  
          self.rand_offset: np.ndarray
          self.magnitude = 1.0
          self.sigma = 1.0
  
      def set_random_state(
          self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None
      ) -> "Rand3DElastic":
          self.rand_affine_grid.set_random_state(seed, state)
          super().set_random_state(seed, state)
          return self
  
      def randomize(self, grid_size: Sequence[int]) -> None:
          super().randomize(None)
          if not self._do_transform:
              return None
          self.rand_offset = self.R.uniform(-1.0, 1.0, [3] + list(grid_size)).astype(np.float32, copy=False)
          self.magnitude = self.R.uniform(self.magnitude_range[0], self.magnitude_range[1])
          self.sigma = self.R.uniform(self.sigma_range[0], self.sigma_range[1])
          self.rand_affine_grid.randomize()
  
      def __call__(
          self,
          img: NdarrayOrTensor,
          spatial_size: Optional[Union[Tuple[int, int, int], int]] = None,
          mode: Optional[Union[GridSampleMode, str]] = None,
          padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
          randomize: bool = True,
      ) -> NdarrayOrTensor:
          """
          Args:
              img: shape must be (num_channels, H, W, D),
              spatial_size: specifying spatial 3D output image spatial size [h, w, d].
                  if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                  the transform will use the spatial size of `img`.
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``self.mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``self.padding_mode``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              randomize: whether to execute `randomize()` function first, default to True.
          """
          sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:])
          if randomize:
              self.randomize(grid_size=sp_size)
  
          _device = img.device if isinstance(img, torch.Tensor) else self.device
          grid = create_grid(spatial_size=sp_size, device=_device, backend="torch")
          if self._do_transform:
              if self.rand_offset is None:
                  raise RuntimeError("rand_offset is not initialized.")
              gaussian = GaussianFilter(3, self.sigma, 3.0).to(device=_device)
              offset = torch.as_tensor(self.rand_offset, device=_device).unsqueeze(0)
              grid[:3] += gaussian(offset)[0] * self.magnitude
              grid = self.rand_affine_grid(grid=grid)
          out: NdarrayOrTensor = self.resampler(
              img, grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode
          )
          return out
  
  
  class GridDistortion(Transform):
  
      backend = [TransformBackends.TORCH]
  
      def __init__(
          self,
          num_cells: Union[Tuple[int], int],
          distort_steps: Sequence[Sequence[float]],
          mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
          padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
          device: Optional[torch.device] = None,
      ) -> None:
          """
          Grid distortion transform. Refer to:
          https://github.com/albumentations-team/albumentations/blob/master/albumentations/augmentations/transforms.py
  
          Args:
              num_cells: number of grid cells on each dimension.
              distort_steps: This argument is a list of tuples, where each tuple contains the distort steps of the
                  corresponding dimensions (in the order of H, W[, D]). The length of each tuple equals to `num_cells + 1`.
                  Each value in the tuple represents the distort step of the related cell.
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``"border"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              device: device on which the tensor will be allocated.
  
          """
          self.resampler = Resample(mode=mode, padding_mode=padding_mode, device=device)
          self.num_cells = num_cells
          self.distort_steps = distort_steps
          self.device = device
  
      def __call__(
          self,
          img: NdarrayOrTensor,
          distort_steps: Optional[Sequence[Sequence]] = None,
          mode: Optional[Union[GridSampleMode, str]] = None,
          padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
      ) -> NdarrayOrTensor:
          """
          Args:
              img: shape must be (num_channels, H, W[, D]).
              distort_steps: This argument is a list of tuples, where each tuple contains the distort steps of the
                  corresponding dimensions (in the order of H, W[, D]). The length of each tuple equals to `num_cells + 1`.
                  Each value in the tuple represents the distort step of the related cell.
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``"border"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
  
          """
          distort_steps = self.distort_steps if distort_steps is None else distort_steps
          if len(img.shape) != len(distort_steps) + 1:
              raise ValueError("the spatial size of `img` does not match with the length of `distort_steps`")
  
          all_ranges = []
          num_cells = ensure_tuple_rep(self.num_cells, len(img.shape) - 1)
          for dim_idx, dim_size in enumerate(img.shape[1:]):
              dim_distort_steps = distort_steps[dim_idx]
              ranges = torch.zeros(dim_size, dtype=torch.float32)
              cell_size = dim_size // num_cells[dim_idx]
              prev = 0
              for idx in range(num_cells[dim_idx] + 1):
                  start = int(idx * cell_size)
                  end = start + cell_size
                  if end > dim_size:
                      end = dim_size
                      cur = dim_size
                  else:
                      cur = prev + cell_size * dim_distort_steps[idx]
                  ranges[start:end] = torch.linspace(prev, cur, end - start)
                  prev = cur
              ranges = ranges - (dim_size - 1.0) / 2.0
              all_ranges.append(ranges)
  
          coords = meshgrid_ij(*all_ranges)
          grid = torch.stack([*coords, torch.ones_like(coords[0])])
  
          return self.resampler(img, grid=grid, mode=mode, padding_mode=padding_mode)  # type: ignore
  
  
  class RandGridDistortion(RandomizableTransform):
  
      backend = [TransformBackends.TORCH]
  
      def __init__(
          self,
          num_cells: Union[Tuple[int], int] = 5,
          prob: float = 0.1,
          distort_limit: Union[Tuple[float, float], float] = (-0.03, 0.03),
          mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
          padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
          device: Optional[torch.device] = None,
      ) -> None:
          """
          Random grid distortion transform. Refer to:
          https://github.com/albumentations-team/albumentations/blob/master/albumentations/augmentations/transforms.py
  
          Args:
              num_cells: number of grid cells on each dimension.
              prob: probability of returning a randomized grid distortion transform. Defaults to 0.1.
              distort_limit: range to randomly distort.
                  If single number, distort_limit is picked from (-distort_limit, distort_limit).
                  Defaults to (-0.03, 0.03).
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``"border"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              device: device on which the tensor will be allocated.
  
          """
          RandomizableTransform.__init__(self, prob)
          self.num_cells = num_cells
          if isinstance(distort_limit, (int, float)):
              self.distort_limit = (min(-distort_limit, distort_limit), max(-distort_limit, distort_limit))
          else:
              self.distort_limit = (min(distort_limit), max(distort_limit))
          self.distort_steps: Sequence[Sequence[float]] = ((1.0,),)
          self.grid_distortion = GridDistortion(
              num_cells=num_cells, distort_steps=self.distort_steps, mode=mode, padding_mode=padding_mode, device=device
          )
  
      def randomize(self, spatial_shape: Sequence[int]) -> None:
          super().randomize(None)
          if not self._do_transform:
              return
          self.distort_steps = tuple(
              tuple(1.0 + self.R.uniform(low=self.distort_limit[0], high=self.distort_limit[1], size=n_cells + 1))
              for n_cells in ensure_tuple_rep(self.num_cells, len(spatial_shape))
          )
  
      def __call__(
          self,
          img: NdarrayOrTensor,
          mode: Optional[Union[GridSampleMode, str]] = None,
          padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
          randomize: bool = True,
      ) -> NdarrayOrTensor:
          """
          Args:
              img: shape must be (num_channels, H, W[, D]).
              mode: {``"bilinear"``, ``"nearest"``}
                  Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                  Padding mode for outside grid values. Defaults to ``"border"``.
                  See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              randomize: whether to shuffle the random factors using `randomize()`, default to True.
          """
          if randomize:
              self.randomize(img.shape[1:])
          if not self._do_transform:
              return img
          return self.grid_distortion(img, distort_steps=self.distort_steps, mode=mode, padding_mode=padding_mode)
  
apis: torch.ones_like numpy.eye numpy.allclose numpy.linalg.solve numpy.meshgrid numpy.append torch.flip torch.as_tensor torch.linspace numpy.argsort numpy.asarray numpy.flip torch.zeros torch.eye numpy.array torch.linalg.solve torch.solve