// low tolerances
    enable_tensor_float_32_execution(false);
  }

  AbstractContextPtr ctx_;

 public:
  bool UseMlir() const { return strcmp(std::get<0>(GetParam()), "mlir") == 0; }
  bool UseFunction() const { return std::get<2>(GetParam()); }
};

TEST_P(GradientCheckerTest, TestMatMul) {
  float A_vals[] = {1.0f, 2.0f, 3.0f, 4.0f};

                                 const void* const* values,
                                 const size_t* lengths,
                                 int num_values) override {
    if (strcmp(attr_name, tensorflow::kColocationAttrName) == 0) {
      op_->colocation_constraints.clear();
      for (int i = 0; i < num_values; ++i) {
        op_->colocation_constraints.emplace(static_cast<const char*>(values[i]),

    if (!CreateInput(inputs[i], cond_graph,
                     absl::StrCat("cond_input", i).c_str(), &cond_inputs[i],
                     status)) {
      break;
    }
    if (!CreateInput(inputs[i], body_graph,
                     absl::StrCat("body_input", i).c_str(), &body_inputs[i],
                     status)) {
      break;
    }
  }

    int len = string.length();
    char[] strCopy = new char[len * Integer.highestOneBit(count)];
    string.getChars(0, len, strCopy, 0);

    char[] array = new char[len * count];

    int strCopyLen = len;
    int pos = 0;
    while (count != 0) {
      if ((count & 1) != 0) {
        System.arraycopy(strCopy, 0, array, pos, strCopyLen);
        pos += strCopyLen;
      }
      count >>= 1;

    int len = string.length();
    char[] strCopy = new char[len * Integer.highestOneBit(count)];
    string.getChars(0, len, strCopy, 0);

    char[] array = new char[len * count];

    int strCopyLen = len;
    int pos = 0;
    while (count != 0) {
      if ((count & 1) != 0) {
        System.arraycopy(strCopy, 0, array, pos, strCopyLen);
        pos += strCopyLen;
      }
      count >>= 1;

#include "tensorflow/c/eager/tfe_context_internal.h"
#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/strcat.h"

// =============================================================================
// Public C API entry points
// These are only the entry points specific to the Eager API.

    // The default NumPy-style output can be distractingly long in error
    // messages.
    value_string = absl::StrCat(value_string.substr(0, 100), " [...]");
  }
  absl::Status s;
  const char* device_name = DeviceName(&s);
  if (!s.ok()) {
    device_name = "<error fetching device name>";
  }
  return absl::StrCat("TensorHandle(", value_string, ", shape=", shape_string,

    lines = [json.loads(line) for line in response.text.strip().splitlines()]
    assert lines == [{"name": "foo"}]


def test_stream_bare_sync_iterable():
    response = client.get("/items/stream-bare-sync")
    assert response.status_code == 200
    assert response.headers["content-type"] == "application/jsonl"
    lines = [json.loads(line) for line in response.text.strip().splitlines()]

    data_lines = [
        line for line in response.text.strip().split("\n") if line.startswith("data: ")
    ]
    assert len(data_lines) == 3


def test_dict_items(client: TestClient):
    response = client.get("/items/stream-dict")
    assert response.status_code == 200
    data_lines = [
        line for line in response.text.strip().split("\n") if line.startswith("data: ")
    ]

		message:    "Time stamp format pattern contains a valid format symbol that cannot be applied to time stamp parsing in the SQL expression.",
		statusCode: 400,
		cause:      err,
	}
}

func errEvaluatorTimestampFormatPatternDuplicateFields(err error) *s3Error {
	return &s3Error{
		code:       "EvaluatorTimestampFormatPatternDuplicateFields",