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: Static method, executes the list of transformations for a given data point.
  
  Parameters
  ----------
  transforms : list
      List of transformations to execute
  data : DataContainer or dict
      Data to be augmented. See ``solt.data.DataContainer.from_dict``
      to check how a conversion from dict is done.
  optimize_stack : bool
      Whether to execute augmentations stack optimization.
  
  Returns
  -------
  out : DataContainer
      Result
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code: def exec_stream(transforms, data, optimize_stack):
      """
      Static method, executes the list of transformations for a given data point.
  
      Parameters
      ----------
      transforms : list
          List of transformations to execute
      data : DataContainer or dict
          Data to be augmented. See ``solt.data.DataContainer.from_dict``
          to check how a conversion from dict is done.
      optimize_stack : bool
          Whether to execute augmentations stack optimization.
  
      Returns
      -------
      out : DataContainer
          Result
      """
  
      data = BaseTransform.wrap_data(data)
      # Performing the transforms using the optimized stack
      if optimize_stack:
          transforms = Stream.optimize_transforms_stack(
              transforms, data
          )
      for trf in transforms:
          if isinstance(trf, BaseTransform):
              if not optimize_stack:
                  data = trf(data, return_torch=False)
              else:
                  data = trf.apply(data)
          elif isinstance(trf, Stream):
              data = trf(data, return_torch=False)
          else:
              raise TypeError(
                  "Unknown transform type found in the Stream!"
              )
      return data
  
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 7 for calling the Stream obj's underlying member method code with example usage: obj.exec_stream(transforms, data, optimize_stack) and returns: data