t3toolbox.backend.contractions.dMNi_dMio_to_dMNo
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.. py:function:: t3toolbox.backend.contractions.dMNi_dMio_to_dMNo(dMNi: t3toolbox.backend.common.NDArray, dMio: t3toolbox.backend.common.NDArray, use_jax: bool = False) -> t3toolbox.backend.common.NDArray

   Computes named contraction.

   N and M may be individual indices, groups of indices, or nonexistent.

   .. rubric:: Examples

   Vectorize over both N and M:

   >>> import numpy as np
   >>> from t3toolbox.utils.contractions import dMNi_dMio_to_dMNo
   >>> dMNi = np.random.randn(8, 5,6, 2,3,4, 10)
   >>> dMio = np.random.randn(8, 5,6, 10,13)
   >>> result = dMNi_dMio_to_dMNo(dMNi, dMio)
   >>> result2 = np.einsum('duvxyzi,duvio->duvxyzo', dMNi, dMio)
   >>> 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 dMNi_dMio_to_dMNo
   >>> dMNi = np.random.randn(8, 2,3,4, 10)
   >>> dMio = np.random.randn(8, 10,13)
   >>> result = dMNi_dMio_to_dMNo(dMNi, dMio)
   >>> result2 = np.einsum('dxyzi,dio->dxyzo', dMNi, dMio)
   >>> 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 dMNi_dMio_to_dMNo
   >>> dMNi = np.random.randn(8, 5,6, 10)
   >>> dMio = np.random.randn(8, 5,6, 10,13)
   >>> result = dMNi_dMio_to_dMNo(dMNi, dMio)
   >>> result2 = np.einsum('duvi,duvio->duvo', dMNi, dMio)
   >>> 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 dMNi_dMio_to_dMNo
   >>> dMNi = np.random.randn(8, 10)
   >>> dMio = np.random.randn(8, 10,13)
   >>> result = dMNi_dMio_to_dMNo(dMNi, dMio)
   >>> result2 = np.einsum('di,dio->do', dMNi, dMio)
   >>> print(result.shape == result2.shape)
   True
   >>> print(np.linalg.norm(result - result2))
   0.0