InvalidArgument("Node ", node->name(), " was not found in the graph");
    return -1;
  }

  tensorflow::shape_inference::ShapeHandle shape = ic->output(output.index);

  // Unknown rank means the number of dimensions is -1.
  if (!ic->RankKnown(shape)) {
    return -1;
  }

  return ic->Rank(shape);
}

void TF_GraphGetTensorShape(TF_Graph* graph, TF_Output output, int64_t* dims,

  if (input_arg.number_attr().empty()) {
    status->status = tensorflow::errors::NotFound(
        op_name, " does not have number_attr() defined.");
    return nullptr;
  }

  // The returned string is owned by OpRegistry, so liveness is not a concern.
  return input_arg.number_attr().c_str();
}

int TF_OpIsStateful(const char* op_type, TF_Status* status) {

    forward_op_->outputs.push_back(retvals[i]);
  }
  // TODO(b/166669239): This is needed to support AttrBuilder::Get for string
  // attributes. Number type attrs and DataType attrs work fine without this.
  // Consider getting rid of this and making the behavior between number types
  // and string consistent.
  forward_op_->attrs.BuildNodeDef();
  std::unique_ptr<GradientFunction> gradient_fn;

      theta_inputs[input_index];  // parameter we are grad checking

  // Convert from AbstractTensor to TF_Tensor.
  TF_Tensor* theta_tensor;
  TF_RETURN_IF_ERROR(GetValue(theta, &theta_tensor));

  // Get number of elements and fill data.
  int num_elems = TF_TensorElementCount(theta_tensor);
  vector<float> theta_data(num_elems);
  memcpy(theta_data.data(), TF_TensorData(theta_tensor),
         TF_TensorByteSize(theta_tensor));

                       TP_OPTIMIZER_REGISTRATION_PARAMS_STRUCT_SIZE);
  VALIDATE_MEMBER(TP_OptimizerRegistrationParams, params, device_type);
  return absl::OkStatus();
}

absl::Status ValidateTPOptimizer(const TP_Optimizer& optimizer) {
  VALIDATE_STRUCT_SIZE(TP_Optimizer, optimizer, TP_OPTIMIZER_STRUCT_SIZE);
  VALIDATE_MEMBER(TP_Optimizer, optimizer, optimize_func);
  return absl::OkStatus();
}

        first_bad_status.reset(TF_NewStatus());
        TF_SetStatus(first_bad_status.get(), TF_INTERNAL,
                     "Parallel ops produced different numbers of tensors.");
      }
    }
  }
  if (first_bad_status != nullptr) {
    TF_SetStatus(status, TF_GetCode(first_bad_status.get()),
                 TF_Message(first_bad_status.get()));
    return result;

#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/platform/base64.h"
#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/util/debug_data_dumper.h"

using tensorflow::errors::InvalidArgument;

namespace tensorflow {
namespace {

Status ValidateNonRefOutput(const Node* node, int idx) {
  const DataType& dt = node->output_type(idx);

constexpr uint64_t kDefaultMaxStaleness = 0;

constexpr char kStatCacheMaxAge[] = "GCS_STAT_CACHE_MAX_AGE";
constexpr uint64_t kStatCacheDefaultMaxAge = 5;
// The environment variable that overrides the maximum number of entries in the
// Stat cache.
constexpr char kStatCacheMaxEntries[] = "GCS_STAT_CACHE_MAX_ENTRIES";
constexpr size_t kStatCacheDefaultMaxEntries = 1024;

// How to upload new data when Flush() is called multiple times.

    return errors::Unimplemented(
        "Filesystem does not support SetConfiguration()");
  }
  if (values.empty()) {
    return errors::InvalidArgument(
        "SetConfiguration() needs number of values > 0");
  }

  TF_Filesystem_Option option;
  memset(&option, 0, sizeof(option));
  option.name = const_cast<char*>(name.c_str());
  TF_Filesystem_Option_Value option_value;

  }

  void VerifyFDefNodes(const tensorflow::FunctionDef& fdef,
                       const std::unordered_set<string>& nodes) {
    ASSERT_EQ(nodes.size(), fdef.node_def_size())
        << "Got unexpected number of nodes. Expected: ["
        << absl::StrJoin(nodes, ", ")
        << "] Actual nodes in fdef: " << fdef.DebugString();
    for (const NodeDef& node_def : fdef.node_def()) {