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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code: def __init__(
      self,
      transforms=None,
      interpolation=None,
      padding=None,
      optimize_stack=False,
      ignore_fast_mode=False,
  ):
      super(Stream, self).__init__()
  
      if transforms is None:
          transforms = []
  
      for trf in transforms:
          if not isinstance(trf, BaseTransform) and not isinstance(
              trf, Stream
          ):
              raise TypeError
      self.optimize_stack = optimize_stack
      self.interpolation = interpolation
      self.padding = padding
      self.ignore_fast_mode = ignore_fast_mode
      self.transforms = transforms
  
      self.reset_ignore_fast_mode(ignore_fast_mode)
      self.reset_padding(padding)
      self.reset_interpolation(interpolation)
  
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 1 to create a new Stream object from inherited base classes: Serializable with example: obj = Stream(transforms, interpolation, padding, optimize_stack, ignore_fast_mode)