[(CopyAttrs $src, $dest)]>;

// Canonicalize: Log(1.0 + x) to Log1p(x)
//
// We currently do this rewrite only if the constant `1` is a scalar, because
// it is safely broadcastable to any shape. To be able to canonicalize when
// constant values is not a scalar, we have to first prove that it is
// broadcastable to `x`, which requires static shape information.
def LogToLog1p : Pat<
  (TF_LogOp:$src (TF_AddV2Op $arg,

    result.push_back(dt);
  }
  return result;
}

std::vector<std::vector<int64_t>> InterestingShapes() {
  std::vector<std::vector<int64_t>> interesting_shapes;
  interesting_shapes.push_back({});             // Scalar
  interesting_shapes.push_back({10});           // 1D Vector
  interesting_shapes.push_back({3, 3});         // 2D Matrix
  interesting_shapes.push_back({1, 4, 6, 10});  // Higher Dimension Tensor
  return interesting_shapes;

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

  // Construct a scalar.
  impl::TaggedValueTensor x;
  {
    AbstractTensorHandle* x_raw = nullptr;
    Status s = TestScalarTensorHandle<float, TF_FLOAT>(ctx.get(), 2.0f, &x_raw);
    ASSERT_EQ(tensorflow::errors::OK, s.code()) << s.message();

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        Tensor t =
            Tensor(tensorflow::DT_FLOAT, tensorflow::TensorShape({100, 50}));
        t.flat<float>().setZero();
        outputs->push_back(t);
      } else {
        // Create a scalar float tensor.
        Tensor t = Tensor(tensorflow::DT_FLOAT, tensorflow::TensorShape({}));
        t.flat<float>()(0) = 1.0f;
        outputs->push_back(t);
      }
    }
    return OkStatus();
  }
};

		m.Name = "/sched/gomaxprocs:threads"
		m.Value = Value{kind: ValueUint64, scalar: e.base.args[0]}
	case go122.EvHeapAlloc:
		m.Name = "/memory/classes/heap/objects:bytes"
		m.Value = Value{kind: ValueUint64, scalar: e.base.args[0]}
	case go122.EvHeapGoal:
		m.Name = "/gc/heap/goal:bytes"
		m.Value = Value{kind: ValueUint64, scalar: e.base.args[0]}
	default:

  //
  // 1) `atomic`: the list is treated as a single entity, like a scalar.
  //      Atomic lists will be entirely replaced when updated. This extension
  //      may be used on any type of list (struct, scalar, ...).
  // 2) `set`:
  //      Sets are lists that must not have multiple items with the same value. Each
  //      value must be a scalar, an object with x-kubernetes-map-type `atomic` or an

  // Construct a parallel tensor consisting of the scalar values from `values`.
  template <typename DataType>
  std::unique_ptr<ParallelTensor> ScalarsFromSequence(
      absl::Span<const DataType> values, TFE_Context* context,
      TF_Status* status) const;

  // A parallel tensor with scalar integers numbering component devices.
  std::unique_ptr<ParallelTensor> DeviceIDs(TFE_Context* context,

  return tensorflow::GetTypeFromTFTensorShape({1}, builder.getIntegerType(32));
}

Value ReshapeScalarToSizeType(OpBuilder builder, Value scalar, Location loc) {
  auto size_type = GetSizeType(builder);
  return builder.create<TF::ReshapeOp>(
      loc, ArrayRef<Type>{size_type},
      ArrayRef<Value>{scalar, GetR1Const(size_type.getShape(), builder, loc)});
}

LogicalResult CreateInitBufferValue(ArrayRef<int64_t> element_shape,

    CHECK_EQ(TF_OK, TF_GetCode(status)) << "Error while adding type constraint";
    TF_RegisterKernelBuilder("ScalarSummary", builder, status);
    CHECK_EQ(TF_OK, TF_GetCode(status))
        << "Error while registering Scalar Summmary kernel";
  }
  TF_DeleteStatus(status);
}

// A dummy static variable initialized by a lambda whose side-effect is to
// register the ScalarSummary kernel.