with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

// is not interested in).
//
// BackwardFunction needs to be a closure which stores intermediate activations
// from the forward computation and calls a vector-jacobian product function
// (also known as adjoint function) to compute, given downstream gradients,
// upstream gradients.
//
// TODO(apassos) provide concrete template instantiations for TFE_TensorHandle

      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

	// n&1 == 0 && n >= karatsubaThreshold && n >= 2

	// Karatsuba multiplication is based on the observation that
	// for two numbers x and y with:
	//
	//   x = x1*b + x0
	//   y = y1*b + y0
	//
	// the product x*y can be obtained with 3 products z2, z1, z0
	// instead of 4:
	//
	//   x*y = x1*y1*b*b + (x1*y0 + x0*y1)*b + x0*y0
	//       =    z2*b*b +              z1*b +    z0
	//
	// with:
	//
	//   xd = x1 - x0

      fail();
    } catch (IllegalArgumentException expected) {
    }
  }

  /** The 0-ary cartesian product is a single empty list. */
  public void testCartesianProduct_zeroary() {
    assertThat(Sets.cartesianProduct()).containsExactly(list());
  }

  /** A unary cartesian product is one list of size 1 for each element in the input set. */
  public void testCartesianProduct_unary() {

    const int64_t batch_size = input_shape[0];

    // Compute the product of the block_shape values.
    int64_t block_num_elems = 1;

    for (auto val : block_shape.getValues<APInt>()) {
      block_num_elems *= val.getSExtValue();
    }

    if (block_num_elems <= 0) {
      op.emitOpError()
          << "The product of the block dimensions must be positive";
      return failure();
    }

      fail();
    } catch (IllegalArgumentException expected) {
    }
  }

  /** The 0-ary cartesian product is a single empty list. */
  public void testCartesianProduct_zeroary() {
    assertThat(Sets.cartesianProduct()).containsExactly(list());
  }

  /** A unary cartesian product is one list of size 1 for each element in the input set. */
  public void testCartesianProduct_unary() {

## Opinie

"_[...] I'm using **FastAPI** a ton these days. [...] I'm actually planning to use it for all of my team's **ML services at Microsoft**. Some of them are getting integrated into the core **Windows** product and some **Office** products._"

<div style="text-align: right; margin-right: 10%;">Kabir Khan - <strong>Microsoft</strong> <a href="https://github.com/tiangolo/fastapi/pull/26" target="_blank"><small>(ref)</small></a></div>

---