---

Exemplos comuns de limite de CPU são coisas que exigem processamento matemático complexo.

Por exemplo:

* **Processamento de áudio** ou **imagem**

	//
	// Then the proof of termination is this:
	// On every iteration where we mark the roots as dirty, we add some new module
	// version to the map. The universe of module versions is finite, so we must
	// eventually reach a state in which we do not add any version to the map.
	// In that state, we either report a conflict or succeed in the edit.
	rejectedRoot := map[module.Version]bool{}

 * infinite value and itself, the infinite value is returned. If required to do a weighted average
 * between {@link Double#NEGATIVE_INFINITY NEGATIVE_INFINITY} and {@link Double#POSITIVE_INFINITY
 * POSITIVE_INFINITY}, {@link Double#NaN NaN} is returned (note that this will only happen if the
 * dataset contains no finite values).
 *
 * <h3>Performance</h3>
 *

	}

	// NOTE: there are other fields like `selector` which we could wipe.
	// Historically we did not wipe them and they are not allocated from
	// finite pools, so we are (currently) choosing to leave them alone.

	// Clear the load-balancer status if it is no longer appropriate.  Although
	// LB de-provisioning is actually asynchronous, we don't need to expose the

  # Check default quantization option values for weight-only quantization.
  # TODO(b/242805842): Find good minimum_elements_for_weights number for server.
  # please also update default value in tflite converter:
  # tensorflow/compiler/mlir/lite/tf_to_tfl_flatbuffer.cc;l=201
  if quantization_options.min_num_elements_for_weights == 0:
    quantization_options.min_num_elements_for_weights = (

TEST_F(NNGradTest, LogSoftmaxGrad) {
  TensorShape shape({5, 3});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto y = LogSoftmax(scope_, x);
  // Avoid numerical instability when computing finite differences.
  Tensor x_init_value =
      test::AsTensor<float>({-0.9f, -0.7f, -0.5f, -0.3f, -0.1f, 0.1f, 0.3f,
                             0.5f, 0.7f, 0.8f, -0.1f, 0.1f, 0.1f, 0.1f, 1.2f},

labels.facet_filetype_fb2=FictionBook
labels.facet_filetype_epub=EPUB
labels.facet_filetype_ibooks=iBooks
labels.facet_filetype_txt=Einfacher Text
labels.facet_filetype_rtf=Rich Text Format
labels.facet_filetype_chm=Kompilierte HTML-Hilfe
labels.facet_filetype_zip=ZIP-Archiv
labels.facet_filetype_7z=7z-Archiv
labels.facet_filetype_bz=Bz-Archiv
labels.facet_filetype_bz2=Bz2-Archiv
labels.facet_filetype_tar=TAR-Archiv

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

  TensorShape shape({3, 2});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto y = Maximum(scope_, x, Const(scope_, 1.0f));
  // Select values away from 1.0f to avoid instability when computing
  // finite differences.
  Tensor x_init_value =
      test::AsTensor<float>({0.5f, 1.5f, -1.2f, 3.0f, 0.1f, 2.8f}, {3, 2});
  RunTest(x, x_init_value, y, shape);
}

TEST_F(NaryGradTest, Minimum) {

 * </ul>
 *
 * <p><b>Note:</b> In general, {@code ByteSource} is intended to be used for "file-like" sources
 * that provide streams that are:
 *
 * <ul>
 *   <li><b>Finite:</b> Many operations, such as {@link #size()} and {@link #read()}, will either
 *       block indefinitely or fail if the source creates an infinite stream.