// Calc Grad
    AbstractTensorHandle* matmul_A_output;
    AbstractTensorHandle* matmul_B_output;
    std::string name_grad_A = "MatMul_Grad_A";
    std::string name_grad_B = "MatMul_Grad_B";
    if (!t_a && !t_b) {
      TF_RETURN_IF_ERROR(MatMul(ctx, upstream_grad, B.get(), &matmul_A_output,
                                /*transpose_a = */ false,

              absl::Span<AbstractTensorHandle* const> inputs,
              absl::Span<AbstractTensorHandle*> outputs) -> Status {
        return ops::MatMul(ctx, inputs[0], inputs[1], &outputs[0], transpose_a,
                           transpose_b, "MatMul");
      };
      ASSERT_NO_FATAL_FAILURE(CompareNumericalAndAutodiffGradients(
          MatMulModel, BuildGradModel(MatMulModel, registry_),

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EXPERIMENTAL_GRADIENTS_MATH_GRAD_H_
#define TENSORFLOW_C_EXPERIMENTAL_GRADIENTS_MATH_GRAD_H_

#include "tensorflow/c/eager/gradients.h"

namespace tensorflow {
namespace gradients {

GradientFunction* AddRegisterer(const ForwardOperation& op);

David P. Baker <******@****.***> 1641482883 -0800

consistent with the original source files registering each operator. For example
since `REGISTER_OP("MatMul")` appears in ***core/math_ops.cc***, the "MatMul"
operator in the script should be in the "math" category, and it will be
generated in the output file `c/experimental/ops/math_ops.cc`.

Running this script should be a no-op, generating identical code other than

     */
    return (int) Math.pow(a, 1.0 / concentration);
  }

  @AfterExperiment
  void tearDown() {
    double req = requests.get();
    double hit = req - misses.get();

    // Currently, this is going into /dev/null, but I'll fix that
    System.out.println("hit rate: " + hit / req);
  }

  // for proper distributions later:
  // import JSci.maths.statistics.ProbabilityDistribution;

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

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

     */
    return (int) Math.pow(a, 1.0 / concentration);
  }

  @AfterExperiment
  void tearDown() {
    double req = requests.get();
    double hit = req - misses.get();

    // Currently, this is going into /dev/null, but I'll fix that
    System.out.println("hit rate: " + hit / req);
  }

  // for proper distributions later:
  // import JSci.maths.statistics.ProbabilityDistribution;

  }

  TFE_Op* matmul = nullptr;
  if (func) {
    const string matmul_device = remote_func_outputs ? task2_name : "";
    string function_def = MatMulFunction(matmul_device);
    TFE_ContextAddFunctionDef(ctx, function_def.data(), function_def.size(),
                              status);
    CHECK_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

    matmul = TFE_NewOp(ctx, "MatMulFunction", status);

        versionProperties["spock"] = "2.2-groovy-${groovyVersion.major}.${groovyVersion.minor}"

        findLatest("guava", "com.google.guava:guava:(20,)", versionProperties)
        findLatest("commons-math", "org.apache.commons:commons-math3:latest.release", versionProperties)
        findLatest("commons-text", "org.apache.commons:commons-text:latest.release", versionProperties)