std::string shape_string;
  if (Shape(&shape).ok()) {
    shape_string = shape.DebugString();
  } else {
    shape_string = "<error computing shape>";
  }
  std::string value_string;
  if (!SummarizeValue(value_string).ok()) {
    value_string = "<error computing value>";
  }
  if (value_string.length() > 100) {
    // The default NumPy-style output can be distractingly long in error
    // messages.

---

<div align="center">
    <img src="https://raw.githubusercontent.com/cncf/artwork/master/other/cncf/horizontal/color/cncf-color.svg" width="300" alt="Cloud Native Computing Foundation logo"/>
    <p>Istio is a <a href="https://cncf.io">Cloud Native Computing Foundation</a> project.</p>

namespace tensorflow {

std::string AbstractTensorHandle::DebugString() const {
  PartialTensorShape shape;
  Status s = Shape(&shape);
  std::string shape_string;
  if (!s.ok()) {
    shape_string = "<error computing shape>";
  } else {
    shape_string = shape.DebugString();
  }
  return absl::StrCat("TensorHandle(shape=", shape_string,
                      ", dtype=", DataType_Name(DataType()),

  TF_RETURN_IF_ERROR(
      ops::IdentityN(ctx, inputs, absl::MakeSpan(temp_outputs), "IdentityN"));
  // Although, `ops::IdentityN` returns 2 tensors, the first tensor isn't needed
  // for computing gradient so we could safely drop it.
  outputs[0] = temp_outputs[1];
  temp_outputs[0]->Unref();
  return absl::OkStatus();
}

class CppGradients

//go:build fips
// +build fips

package sha256

import (
	fipssha256 "crypto/sha256"
	"hash"
)

// New returns a new hash.Hash computing the SHA256 checksum.
// The SHA256 implementation is FIPS 140-2 compliant when the
// boringcrypto branch of Go is used.
// Ref: https://github.com/golang/go/tree/dev.boringcrypto
func New() hash.Hash { return fipssha256.New() }

//go:build !fips
// +build !fips

package sha256

import (
	"hash"

	nofipssha256 "github.com/minio/sha256-simd"
)

// New returns a new hash.Hash computing the SHA256 checksum.
// The SHA256 implementation is not FIPS 140-2 compliant.
func New() hash.Hash { return nofipssha256.New() }

// Sum256 returns the SHA256 checksum of the data.

  // `gradient_checker` only works with model that returns only 1 tensor.
  // Although, `ops::SparseSoftmaxCrossEntropyWithLogits` returns 2 tensors, the
  // second tensor isn't needed for computing gradient so we could safely drop
  // it.
  outputs[0] = loss;
  backprop->Unref();
  return absl::OkStatus();
}

Status BiasAddModel(AbstractContext* ctx,

  // Returns size of specified dimension
  //
  // -1 indicates an unknown axis length; this is unreachable for most standard
  // ImmediateExecutionTensorHandles, but comes up for example when computing
  // the shape of a parallel tensor with component shapes differing across
  // devices.
  virtual Status Dim(int dim_index, int64_t* dim) const = 0;

  // Returns the device which created the handle.

    assertThrows(UncheckedTimeoutException.class, () -> proxy.sleepThenReturnInput("x"));

    assertThat(stopwatch.elapsed(MILLISECONDS)).isIn(Range.closed(NOT_ENOUGH_MS, DELAY_MS * 2));
    // Is it still computing away anyway?
    assertThat(target.finished).isFalse();
    MILLISECONDS.sleep(ENOUGH_MS);
    assertThat(target.finished).isFalse();
  }

  public void testNewProxy_badMethodWithEnoughTime() throws Exception {

      Status s =
          BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);
      ASSERT_EQ(errors::OK, s.code()) << s.message();
      ctx_.reset(ctx_raw);
    }

    // Computing numerical gradients with TensorFloat-32 is numerically
    // unstable. Some forward pass tests also fail with TensorFloat-32 due to
    // low tolerances
    enable_tensor_float_32_execution(false);
  }