fora
gairebé
ha
han
has
havia
he
hem
heu
hi 
ho
i
igual
iguals
ja
l'hi
la
les
li
li'n
llavors
m'he
ma
mal
malgrat
mateix
mateixa
mateixes
mateixos
me
mentre
més
meu
meus
meva
meves
molt
molta
moltes
molts
mon
mons
n'he
n'hi
ne
ni
no
nogensmenys
només
nosaltres

"estàveu", "esteu", "et", "etc", "ets", "fins", "fora", "gairebé", "ha", "han", "has", "havia", "he", "hem", "heu", "hi ", "ho", "i", "igual", "iguals", "ja", "l'hi", "la", "les", "li", "li'n", "llavors", "m'he", "ma", "mal", "malgrat", "mateix", "mateixa", "mateixes", "mateixos", "me", "mentre", "més", "meu", "meus", "meva", "meves", "molt", "molta", "moltes", "molts", "mon", "mons", "n'he", "n'hi", "ne", "ni", "no", "nogensmenys", "només", "nosaltres", "nostra", "nostre", "nostres", "o", "oh", "oi", "on",...

"estàveu", "esteu", "et", "etc", "ets", "fins", "fora", "gairebé", "ha", "han", "has", "havia", "he", "hem", "heu", "hi ", "ho", "i", "igual", "iguals", "ja", "l'hi", "la", "les", "li", "li'n", "llavors", "m'he", "ma", "mal", "malgrat", "mateix", "mateixa", "mateixes", "mateixos", "me", "mentre", "més", "meu", "meus", "meva", "meves", "molt", "molta", "moltes", "molts", "mon", "mons", "n'he", "n'hi", "ne", "ni", "no", "nogensmenys", "només", "nosaltres", "nostra", "nostre", "nostres", "o", "oh", "oi", "on",...

  /* Want to test simple MatMul example:
    [[0,0],    *   [[0,0],    =   [[0,0],
     [0,0]]         [0,0]]         [0,0]]
  */

  // Build an abstract input tensor.
  int64_t dims[] = {2, 2};  // Matrices will be 2 x 2
  int num_dims = sizeof(dims) / sizeof(dims[0]);

  float vals[] = {0.0f, 0.0f, 0.0f, 0.0f};
  TFE_Context* eager_ctx = TF_ExecutionContextGetTFEContext(ctx, status.get());
  TFE_TensorHandle* t =

A queue is used to give a few properties:
* Ability to serially process updates received from a variety of different sources. This avoids need for other synchronization mechanisms like mutexes.
* Correctness at startup; with the sequencing above, items are only processed once all informers are synced. This means queries will not return stale data at startup.
* Deduping of identical events

* **Machine Learning**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande folha de papel com números e multiplicando todos eles juntos e ao mesmo tempo.

  TFE_DeleteContextOptions(opts);

  TFE_ContextSetServerDef(ctx, 0, serialized.data(), serialized.size(), status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Use large matrices so that RPCs don't return before we get a chance
  // to call TFE_DeleteContext.
  TFE_TensorHandle* h0_task0 = TestMatrixTensorHandle100x100(ctx);
  TFE_TensorHandle* h1_task0 = TestMatrixTensorHandle100x100(ctx);

* L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.

* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Imagina en una enorme hoja de cálculo con números y tener que multiplicarlos todos al mismo tiempo.

    matrices or batches of matrices (CPU only).
*   Added gradients for eigenvalues and eigenvectors computed using
    `self_adjoint_eig` or `self_adjoint_eigvals`.
*   Eliminated `batch_*` methods for most linear algebra and FFT ops and
    promoted the non-batch version of the ops to handle batches of matrices.