class_docstr: Stream class. Executes the list of transformations.
  
  The stream can be called directly on a dict of items,
  which get converted into a DataContainer. Note that the DataContainer assumes
  **the same** coordinate system for all the items it is storing.
  For instance, the behavior shown below is not supported for any stream:
  
  .. highlight:: python
  .. code-block:: python
  
      import solt.transforms as slt
      import numpy as np
  
      trf = solt.Stream([slt.Resize(resize_to=(50, 50)),
                             slt.Flip()])
      img1 = np.ones((100, 100, 3), dtype=np.int32)
      img2 = np.ones((110, 110, 3), dtype=np.int32)
      trf({'images': (img1, img2)})
  
  Parameters
  ----------
  transforms : list or None
      List of transforms to execute
  interpolation : str or None
      Stream-wide settings for interpolation. If for some particular transform your would like
      to still use its own mode, simply pass ``(<interpolation_value>, 'strict')``
      in the constructor of that transform.
  padding : str or None
      Stream-wide settings for padding. If for some particular transform your would like
      to still use its own mode, simply pass ``(<padding_value>, 'strict')``
      in the constructor of that transform.
  optimize_stack : bool
      Whether to run transforms stack optimization. It can only be useful if many matrix transformations are
      in a row.
  ignore_fast_mode : bool
      Whether to ignore the fast mode. This option enables full geometric transforms.
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docstr: Executes the list of the pre-defined transformations for a given data container.
  
  Parameters
  ----------
  data : DataContainer or dict
      Data to be augmented. See ``solt.core.DataContainer.from_dict`` for details.
  return_torch : bool
      Whether to convert the result into a torch tensors. By default, it is false for transforms and
      true for the streams.
  as_dict : bool
      Whether to pool the results into a dict. See ``solt.core.DataContainer.to_dict`` for details
  scale_keypoints : bool
      Whether to scale the keypoints into 0-1 range
  normalize : bool
      Whether to normalize the resulting tensor. If mean or std args are None,
      ImageNet statistics will be used
  mean : None or tuple of float or np.ndarray or torch.FloatTensor
      Mean to subtract for the converted tensor
  std : None or tuple of float or np.ndarray or torch.FloatTensor
      Mean to subtract for the converted tensor
  
  Returns
  -------
  out : DataContainer or dict or list
      Result
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code: def __call__(
      self,
      data,
      return_torch=True,
      as_dict=True,
      scale_keypoints=True,
      normalize=True,
      mean=None,
      std=None,
  ):
      """
      Executes the list of the pre-defined transformations for a given data container.
  
      Parameters
      ----------
      data : DataContainer or dict
          Data to be augmented. See ``solt.core.DataContainer.from_dict`` for details.
      return_torch : bool
          Whether to convert the result into a torch tensors. By default, it is false for transforms and
          true for the streams.
      as_dict : bool
          Whether to pool the results into a dict. See ``solt.core.DataContainer.to_dict`` for details
      scale_keypoints : bool
          Whether to scale the keypoints into 0-1 range
      normalize : bool
          Whether to normalize the resulting tensor. If mean or std args are None,
          ImageNet statistics will be used
      mean : None or tuple of float or np.ndarray or torch.FloatTensor
          Mean to subtract for the converted tensor
      std : None or tuple of float or np.ndarray or torch.FloatTensor
          Mean to subtract for the converted tensor
  
      Returns
      -------
      out : DataContainer or dict or list
          Result
  
      """
  
      res: DataContainer = Stream.exec_stream(
          self.transforms, data, self.optimize_stack
      )
  
      if return_torch:
          return res.to_torch(
              as_dict=as_dict,
              scale_keypoints=scale_keypoints,
              normalize=normalize,
              mean=mean,
              std=std,
          )
      return res
  
file_path: solt/solt/core/_core.py
imports: import numpy
  from ._base_transforms import BaseTransform, MatrixTransform
  import copy
  import random
  from solt.utils import Serializable
  from ._data import DataContainer
label_desc: Use image node_id 5 for calling the Stream obj's underlying member method code with example usage: obj.__call__(data, return_torch, as_dict, scale_keypoints, normalize, mean, std) and returns: res, res