TEST_P(GradientCheckerTest, TestMatMul) {
  float A_vals[] = {1.0f, 2.0f, 3.0f, 4.0f};
  int64_t A_dims[] = {2, 2};
  AbstractTensorHandlePtr A;
  {
    AbstractTensorHandle* A_raw;
    absl::Status s = TestTensorHandleWithDims<float, TF_FLOAT>(
        ctx_.get(), A_vals, A_dims, 2, &A_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    A.reset(A_raw);
  }
  float B_vals[] = {.5f, -1.0f, 1.0f, 1.0f};

         TF_TensorByteSize(result_tensor));

  // Build expected result & verify.
  float e_vals[] = {19.0f, 22.0f, 43.0f, 50.0f};

  int data_len = 4;  // length of e_vals
  for (int i = 0; i < data_len; i++) {
    EXPECT_EQ(result_data[i], e_vals[i]);
  }

  TF_DeleteTensor(result_tensor);
  TF_DeleteAbstractTensor(result);
  TF_DeleteOutputList(o);

  AbstractTensorHandlePtr sum_dims;
  {
    vector<int32_t> vals(num_dims_out);
    int64_t vals_shape[] = {num_dims_out};
    Range(&vals, 0, num_dims_out);
    AbstractTensorHandle* sum_dims_raw = nullptr;
    TF_RETURN_IF_ERROR(TestTensorHandleWithDims<int32_t, TF_INT32>(
        ctx, vals.data(), vals_shape, 1, &sum_dims_raw));
    sum_dims.reset(sum_dims_raw);
  }

					gr.Close()
				},
			}

			opts, err := batchReplicationOpts(ctx, "", gr.ObjInfo)
			if err != nil {
				batchLogIf(ctx, err)
				continue
			}

			for k, vals := range opts.Header() {
				for _, v := range vals {
					snowballObj.Headers.Add(k, v)
				}
			}

			input <- snowballObj
		}
	}()

	// Collect and upload all entries.