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.

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

  // `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,

    checkNonnegative(distance, "distance");
    for (long i = 0; i < distance; i++) {
      current = next(current);
      if (current == null) {
        throw new IllegalArgumentException(
            "overflowed computing offset(" + origin + ", " + distance + ")");
      }
    }
    return current;
  }

  /**
   * Returns the unique least value of type {@code C} that is greater than {@code value}, or {@code

    checkNonnegative(distance, "distance");
    for (long i = 0; i < distance; i++) {
      current = next(current);
      if (current == null) {
        throw new IllegalArgumentException(
            "overflowed computing offset(" + origin + ", " + distance + ")");
      }
    }
    return current;
  }

  /**
   * Returns the unique least value of type {@code C} that is greater than {@code value}, or {@code

        //       -2_147_483_628 -> ...
        //   } }
        // That constant is encoded as `unaryMinus` call with the const 2147483628 of long type, while the resolved type is Int.
        // After computing the compile time constant, we need to adjust its type here.
        val expression =
            if (expectedKind != null && expectedKind != kind && value is Number) {

    ReferenceEntry<K, V> entry = getReferenceEntry(cache, key);
    if (entry != null) {
      ValueReference<K, V> valueRef = entry.getValueReference();
      // fail on strong/computing refs
      Preconditions.checkState(valueRef instanceof Reference);
      Reference<V> ref = (Reference<V>) valueRef;
      if (ref != null) {
        ref.clear();
      }
    }
  }

  /**

    ReferenceEntry<K, V> entry = getReferenceEntry(cache, key);
    if (entry != null) {
      ValueReference<K, V> valueRef = entry.getValueReference();
      // fail on strong/computing refs
      Preconditions.checkState(valueRef instanceof Reference);
      Reference<V> ref = (Reference<V>) valueRef;
      if (ref != null) {
        ref.clear();
      }
    }
  }

  /**

import org.checkerframework.checker.nullness.qual.NonNull;
import org.checkerframework.checker.nullness.qual.Nullable;

/**
 * A strategy for determining whether two instances are considered equivalent, and for computing
 * hash codes in a manner consistent with that equivalence. Two examples of equivalences are the
 * {@linkplain #identity() identity equivalence} and the {@linkplain #equals "equals" equivalence}.
 *
 * @author Bob Lee