class_docstr: Policy-based image augmentation.
docstr: Compute the transformation matrix according to the provided parameters.
  
  Args:
      input: the input tensor.
      params: params for the sequence.
      recompute: if to recompute the transformation matrix according to the params.
          default: False.
docstr_tok: ["Compute","the","transformation","matrix","according","to","the","provided","parameters",".","Args",":","input",":","the","input","tensor",".","params",":","params","for","the","sequence",".","recompute",":","if","to","recompute","the","transformation","matrix","according","to","the","params",".","default",":","False","."]
code: def get_transformation_matrix(
      self,
      input: Tensor,
      params: Optional[List[ParamItem]] = None,
      recompute: bool = False,
      extra_args: Dict[str, Any] = {},
  ) -> Optional[Tensor]:
      """Compute the transformation matrix according to the provided parameters.
  
      Args:
          input: the input tensor.
          params: params for the sequence.
          recompute: if to recompute the transformation matrix according to the params.
              default: False.
      """
      if params is None:
          raise NotImplementedError("requires params to be provided.")
      named_modules: Iterator[
          Tuple[str, Module]
      ] = self.get_forward_sequence(params)
  
      # Define as 1 for broadcasting
      res_mat: Optional[Tensor] = None
      for (_, module), param in zip(
          named_modules, params if params is not None else []
      ):
          module = cast(PolicySequential, module)
          mat = module.get_transformation_matrix(
              input,
              params=cast(Optional[List[ParamItem]], param.data),
              recompute=recompute,
              extra_args=extra_args,
          )
          res_mat = mat if res_mat is None else mat @ res_mat
      return res_mat
  
imports: from typing import Any, Dict, Iterator, List, Optional, Tuple, Union, cast
  import torch
  from kornia.augmentation.auto.operations.base import OperationBase
  from kornia.augmentation.auto.operations.policy import PolicySequential
  from kornia.augmentation.container.base import ImageSequentialBase, TransformMatrixMinIn
  from kornia.augmentation.container.ops import InputSequentialOps
  from kornia.augmentation.container.params import ParamItem
  from kornia.core import Module, Tensor
  from kornia.utils import eye_like
label_desc: Use image node_id 7 for calling the PolicyAugmentBase obj's underlying member method code with example usage: obj.get_transformation_matrix(input, params, recompute, extra_args) and returns: res_mat