node {
  name: "Placeholder"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        dim {
          size: 2
        }
        dim {
          size: 5
        }
        dim {

# CHECK-SAME: tf._original_func_name


def Test():
  data = tf.constant([1, 2, 3, 4, 5, 6])
  # Create placeholders to prevent constant folding.
  x_op = tf.placeholder(dtype=tf.int32)
  y_op = tf.placeholder(dtype=tf.int32)
  less_op = tf.less(x_op, y_op)
  switch_op = control_flow_ops.switch(data, less_op)
  merge_op = control_flow_ops.merge(switch_op)[0]
  result = tf.transpose(merge_op)

    Tensor* cpu_tensor, StatusCallback done) {
  DataType dtype = EncodePrimitiveTypeAsDataType(shape_.element_type()).value();
  TensorShape tensor_shape;
  Status status = XLAShapeToTensorShape(shape_, &tensor_shape);
  if (!status.ok()) {
    done(status);
    return;
  }

  *cpu_tensor = Tensor(dtype, tensor_shape);

  status = stream_->Memcpy(cpu_tensor->data(), device_memory_base_,

//sys	DeregisterEventSource(handle Handle) (err error) = advapi32.DeregisterEventSource

                .values();
        for (const StructuredValue& arg : args) {
          PartialTensorShape shape = arg.tensor_spec_value().shape();
          DataType dtype = arg.tensor_spec_value().dtype();
          TaggedValue tensor_spec(shape, dtype);
          input_signature.tuple().emplace_back(tensor_spec);
        }

        // Setup output signature.
        TensorSpecProto output_tensor_spec_proto =

    @Test
    fun `handles the hidden properties correctly`() {
        val aType = schema.dataClassesByFqName.getValue(DefaultFqName.parse(HasHiddenProperty::class.qualifiedName!!))
        assertTrue { aType.properties.single { it.name == "y" }.isHiddenInDsl }

        val result = schema.resolve(
            """
            x = 1
            y = 2

		}
		resp.Header.Set("Location", t.rewriteURL(targetURL, req.URL, req.Host))
		return resp, nil
	}

	cType := resp.Header.Get("Content-Type")
	cType = strings.TrimSpace(strings.SplitN(cType, ";", 2)[0])
	if cType != "text/html" {
		// Do nothing, simply pass through
		return resp, nil
	}

	return t.rewriteResponse(req, resp)
}

          1.0, name="initialized_variable")
      # An UninitializedVariable with the same name as the variable in the
      # SubModule, but with a different type.
      self.uninitialized_variable = resource_variable_ops.UninitializedVariable(
          name="uninitialized_variable", dtype=dtypes.float32)

    @def_function.function(
        input_signature=[tensor_spec.TensorSpec((), dtypes.float32)])
    def compute(self, value):

  class AddOneModel(tf.keras.Model):

    def call(self, x):
      return x + 1

  model = AddOneModel()

  x_train = tf.constant([1, 2, 3, 4, 5], dtype=tf.float32)
  y_train = tf.constant([2, 3, 4, 5, 6], dtype=tf.float32)

  model.compile(optimizer='sgd', loss='mse')
  model.fit(x_train, y_train, epochs=1)

  path = tempdir + '/add_one_model'
  model.save(path)
  return path

	"f15f":     "ecryptfs",
	"794c7630": "overlayfs",
	"2fc12fc1": "zfs",
	"ff534d42": "cifs",
	"53464846": "wslfs",
}

// getFSType returns the filesystem type of the underlying mounted filesystem
func getFSType(ftype int32) string {
	fsTypeHex := strconv.FormatInt(int64(ftype), 16)
	fsTypeString, ok := fsType2StringMap[fsTypeHex]
	if !ok {
		return "UNKNOWN"
	}
	return fsTypeString
}