t3toolbox.backend.contractions.Mio_No_to_MNi
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.. py:function:: t3toolbox.backend.contractions.Mio_No_to_MNi(Mio: t3toolbox.backend.common.NDArray, No: t3toolbox.backend.common.NDArray, use_jax: bool = False) -> t3toolbox.backend.common.NDArray

   Computes vectorized einsum io,o->i, with vectorization over io, o, or both

   N and M are the vectorization indices, which may be groups of indices.

   .. rubric:: Examples

   Vectorize over both N and M:

   >>> import numpy as np
   >>> from t3toolbox.utils.contractions import Mio_No_to_MNi
   >>> Mio = np.random.randn(5,6, 10,13)
   >>> No = np.random.randn(2,3,4, 13)
   >>> result = Mio_No_to_MNi(Mio, No)
   >>> result2 = np.einsum('uvio,xyzo->uvxyzi', Mio, No)
   >>> print(result.shape == result2.shape)
   True
   >>> print(np.linalg.norm(result - result2))
   0.0

   Vectorize over N only

   >>> import numpy as np
   >>> from t3toolbox.utils.contractions import Mio_No_to_MNi
   >>> Mio = np.random.randn(10,13)
   >>> No = np.random.randn(2,3,4, 13)
   >>> result = Mio_No_to_MNi(Mio, No)
   >>> result2 = np.einsum('io,xyzo->xyzi', Mio, No)
   >>> print(result.shape == result2.shape)
   True
   >>> print(np.linalg.norm(result - result2))
   0.0

   Vectorize over both M only:

   >>> import numpy as np
   >>> from t3toolbox.utils.contractions import Mio_No_to_MNi
   >>> Mio = np.random.randn(5,6, 10,13)
   >>> No = np.random.randn(13)
   >>> result = Mio_No_to_MNi(Mio, No)
   >>> result2 = np.einsum('uvio,o->uvi', Mio, No)
   >>> print(result.shape == result2.shape)
   True
   >>> print(np.linalg.norm(result - result2))
   0.0

   No vectorization:

   >>> import numpy as np
   >>> from t3toolbox.utils.contractions import Mio_No_to_MNi
   >>> Mio = np.random.randn(10,13)
   >>> No = np.random.randn(13)
   >>> result = Mio_No_to_MNi(Mio, No)
   >>> result2 = np.einsum('io,o->i', Mio, No)
   >>> print(result.shape == result2.shape)
   True
   >>> print(np.linalg.norm(result - result2))
   0.0