class_docstr: Dictionary version of RandSmoothFieldAdjustIntensity.
  
  The field is randomized once per invocation by default so the same field is applied to every selected key. The
  `mode` parameter specifying interpolation mode for the field can be a single value or a sequence of values with
  one for each key in `keys`.
  
  Args:
      keys: key names to apply the augment to
      spatial_size: size of input arrays, all arrays stated in `keys` must have same dimensions
      rand_size: size of the randomized field to start from
      pad: number of pixels/voxels along the edges of the field to pad with 0
      mode: interpolation mode to use when upsampling
      align_corners: if True align the corners when upsampling field
      prob: probability transform is applied
      gamma: (min, max) range of intensity multipliers
      device: Pytorch device to define field on
code: def set_random_state(
      self,
      seed: int | None = None,
      state: np.random.RandomState | None = None,
  ) -> RandSmoothFieldAdjustIntensityd:
      super().set_random_state(seed, state)
      self.trans.set_random_state(seed, state)
      return self
  
imports: from __future__ import annotations
  from collections.abc import Hashable, Mapping, Sequence
  from typing import Any
  import numpy
  import torch
  from monai.config import KeysCollection, SequenceStr
  from monai.config.type_definitions import NdarrayOrTensor
  from monai.data.meta_obj import get_track_meta
  from monai.transforms.smooth_field.array import RandSmoothDeform, RandSmoothFieldAdjustContrast, RandSmoothFieldAdjustIntensity
  from monai.transforms.transform import MapTransform, RandomizableTransform
  from monai.utils import GridSampleMode, GridSamplePadMode, InterpolateMode, convert_to_tensor, ensure_tuple_rep
label_desc: Use image node_id 2 for calling the RandSmoothFieldAdjustIntensityd obj's underlying member method code with example usage: obj.set_random_state(seed, state) and returns: self