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namespace tensorflow {
namespace gradients {

/* Returns numerical grad inside `dtheta_approx` given `forward` model and
 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],
 * this will calculate dy/dw numerically.
 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will

      Iterable<? extends T>... inputs) {
    return concatNoDefensiveCopy(Arrays.copyOf(inputs, inputs.length));
  }

  /**
   * Returns a fluent iterable that combines several iterables. The returned iterable has an
   * iterator that traverses the elements of each iterable in {@code inputs}. The input iterators
   * are not polled until necessary.
   *

      Iterable<? extends T>... inputs) {
    return concatNoDefensiveCopy(Arrays.copyOf(inputs, inputs.length));
  }

  /**
   * Returns a fluent iterable that combines several iterables. The returned iterable has an
   * iterator that traverses the elements of each iterable in {@code inputs}. The input iterators
   * are not polled until necessary.
   *

                        updater.write("# " + line);
                    }
                    continue;
                }

                inputs = m.group(1).trim().split(",");
                output = m.group(2).trim();

                if (inputs == null || output == null || inputs.length == 0) {
                    logger.warn("Failed to parse mapping: line={}, path={}", line, path);
                    if (updater != null) {

        assertEquals("StopwordsItem [id=123, inputs=word, newInputs=null]", item.toString());

        item.setNewInput("updated");
        assertEquals("StopwordsItem [id=123, inputs=word, newInputs=updated]", item.toString());

        item.setNewInput("");
        assertEquals("StopwordsItem [id=123, inputs=word, newInputs=]", item.toString());
    }

    @Test

                                              TFE_TensorHandle** inputs,
                                              int num_inputs,
                                              TF_Status* status);

// Fetches the current number of inputs attached to `op`.
//
// Does not use the operation's definition to determine how many inputs should
// be attached. It is intended for use with TFE_OpGetFlatInput to inspect an

        assertFalse(result, "Filter should reject any provided name");
        verifyNoInteractions(mockDir);
    }

    /**
     * Edge: name-based filter behavior for diverse inputs.
     */
    @ParameterizedTest
    @DisplayName("name-based filter matches .txt case-insensitively")
    @CsvSource({ "notes.txt,true", "REPORT.TXT,true", "image.png,false", "archive.tar.gz,false", "txt,false" })

	testCases := []struct {
		args       []string
		totalSizes []uint64
		indexes    [][]uint64
		success    bool
	}{
		// Invalid inputs.
		{
			[]string{"data{1...17}/export{1...52}"},
			[]uint64{14144},
			nil,
			false,
		},
		// Valid inputs.
		{
			[]string{"data{1...3}"},
			[]uint64{3},
			[][]uint64{{3}},
			true,
		},
		{

            TaskInputs inputs = task.getInputs();
            inputs.files(extension.getCssFiles())
                .withPropertyName("manual")
                .withPathSensitivity(PathSensitivity.RELATIVE);
            inputs.dir("src/main/resources")
                .withPropertyName("resources")
                .withPathSensitivity(PathSensitivity.RELATIVE);
            inputs.dir(extension.getUserManual().getSnippets())