* (key) bits 'affects' a strictly smaller set of output bits. Funneling is bad because it can
   * result in more-than-ideal collisions for a non-uniformly distributed key space. In practice,
   * most key spaces are ANYTHING BUT uniformly distributed. A bit(i) in the input is said to
   * 'affect' a bit(j) in the output if two inputs, identical but for bit(i), will differ at output

                         absl::Span<AbstractTensorHandle*> outputs) {
  return ops::MatMul(ctx, inputs[0], inputs[1], &outputs[0],
                     /*transpose_a=*/false,
                     /*transpose_b=*/false, "MatMul");
}

absl::Status MulModel(AbstractContext* ctx,
                      absl::Span<AbstractTensorHandle* const> inputs,

    }

    protected EditBody createEditBody(final SynonymItem entity, final String dictId) {
        final EditBody body = new EditBody();
        body.id = entity.getId();
        body.dictId = dictId;
        body.inputs = entity.getInputsValue();
        body.outputs = entity.getOutputsValue();
        return body;
    }

import java.util.Map.Entry;
import junit.framework.TestSuite;

/**
 * A generator of {@code TestSuite} instances for testing {@code CharSource} implementations.
 * Generates tests of all methods on a {@code CharSource} given various inputs the source is
 * expected to contain.
 *
 * @author Colin Decker
 */
@AndroidIncompatible // TODO(b/230620681): Make this available (even though we won't run it).

    AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs,
    absl::Span<AbstractTensorHandle*> outputs) {
  Tape tape(/*persistent=*/false);
  tape.Watch(inputs[0]);
  AbstractTensorHandle* neg_output;
  TF_RETURN_IF_ERROR(ops::Neg(ctx, inputs[0], &neg_output, "Neg"));
  tape.RecordOperation(inputs, {neg_output}, nullptr, "Neg");
  return tape.ComputeGradient(ctx,

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

    TFE_TensorHandle* input = TFE_OpGetFlatInput(original_op, j, s);
    if (TF_GetCode(s) != TF_OK) return;
    const char* input_device = TFE_TensorHandleDeviceName(input, s);
    if (TF_GetCode(s) != TF_OK) return;
    if (dev->device_name == input_device) {
      LoggedTensor* t = reinterpret_cast<LoggedTensor*>(
          TFE_TensorHandleDevicePointer(input, s));
      if (TF_GetCode(s) != TF_OK) return;

                                    <label for="inputs" class="col-sm-3 text-sm-right col-form-label"><la:message
                                            key="labels.dict_mapping_source"/></label>
                                    <div class="col-sm-9">
                                        <la:errors property="inputs"/>
                                        <la:textarea styleId="inputs" property="inputs" rows="5"

ame:"dimension",validatorFunction:function(b,d,e,g,h){if(c){var i=e.get(0).files||[],j=this;e.attr("data-validation").indexOf("mime")===-1?(alert("You should validate file type being jpg, gif or png on input "+e[0].name),b(!1)):i.length>1?(alert("Validating image dimensions does not support inputs allowing multiple files"),b(!1)):0===i.length?b(!0):f(i[0],function(c){var d=!1;e.valAttr("dimension")&&(d=a.formUtils.checkImageDimension(c,e.valAttr("dimension"),h)),!d&&e.valAttr("ratio")&&(d=a.form...

    if (yStats.count() == 0) {
      sumOfProductsOfDeltas = values.sumOfProductsOfDeltas();
    } else {
      // This is a generalized version of the calculation in add(double, double) above. Note that
      // non-finite inputs will have sumOfProductsOfDeltas = NaN, so non-finite values will result
      // in NaN naturally.
      sumOfProductsOfDeltas +=
          values.sumOfProductsOfDeltas()
              + (values.xStats().mean() - xStats.mean())