%1:5 = "tf.PartitionedCall"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5) {Tin = ["tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT"], Tout = ["tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT"], _output_shapes = ["tfshape$dim { size: 9 } dim {...

  %1 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<16x28xf32>} : () -> tensor<16x28xf32>
  %2 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<16x16xf32>} : () -> tensor<16x16xf32>
  %3 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<16xf32>} : () -> tensor<16xf32>

^bb0(%arg0: tensor<1x32x32x16xf32>, %arg1: tensor<*xf32>):
  %0 = "tf.Shape"(%arg0) {T = "tfdtype$DT_FLOAT", output = "tfdtype$DT_INT32"} : (tensor<1x32x32x16xf32>) -> tensor<4xi32>
  %1 = "tf.Shape"(%arg1) {T = "tfdtype$DT_FLOAT", output = "tfdtype$DT_INT32"} : (tensor<*xf32>) -> tensor<?xi32>
  func.return %0, %1 : tensor<4xi32>, tensor<?xi32>
}

// -----

        bias = array_ops.constant(
            np.random.uniform(size=[shapes[1][-1]]), dtype=dtypes.float32
        )
    model = MatmulModel(bias)
    x = array_ops.constant(
        np.random.uniform(size=x_shape), dtype=dtypes.float32
    )
    y = array_ops.constant(
        np.random.uniform(size=y_shape), dtype=dtypes.float32
    )
    if use_kernel:
      model.matmul = model.matmul_with_kernel

        }
        node->AddAttr("shape", index_it->second->second.shape);
        DataType dtype = index_it->second->second.imported_dtype;
        // Uses the existing output type if it isn't specified by the user.
        if (dtype == DT_INVALID) {
          dtype = node->output_type(0);
        }
        node->AddAttr("dtype", dtype);
      }
    }
    if (!node_added_to_shape_refiner) {

          dtype == tensorflow::DT_UINT16 || dtype == tensorflow::DT_INT16 ||
          dtype == tensorflow::DT_UINT8 || dtype == tensorflow::DT_INT8 ||
          dtype == tensorflow::DT_HALF || dtype == tensorflow::DT_BFLOAT16 ||
          dtype == tensorflow::DT_FLOAT || dtype == tensorflow::DT_DOUBLE ||
          dtype == tensorflow::DT_COMPLEX64 ||
          dtype == tensorflow::DT_COMPLEX128 || dtype == tensorflow::DT_BOOL) {
        return {};

  for (auto* n : arg_nodes) {
    int index;
    TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), "index", &index));
    DataType dtype;
    TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), "T", &dtype));
    (*recv_at_host_dtypes)[index] = dtype;
  }
  for (int i = 0, end = recv_at_host_dtypes->size(); i < end; i++) {
    if ((*recv_at_host_dtypes)[i] == DT_INVALID) {

    if (!status->status.ok()) return false;
  }
  return true;
}

// Create an empty tensor of type 'dtype'. 'shape' can be arbitrary, but has to
// result in a zero-sized tensor.
static TF_Tensor* EmptyTensor(TF_DataType dtype,
                              const tensorflow::TensorShape& shape) {
  static char empty;
  int64_t nelems = 1;
  std::vector<int64_t> dims;

      "type": "object"
    },
    "io.k8s.api.core.v1.PortworxVolumeSource": {
      "description": "PortworxVolumeSource represents a Portworx volume resource.",
      "properties": {
        "fsType": {
          "description": "FSType represents the filesystem type to mount Must be a filesystem type supported by the host operating system. Ex. \"ext4\", \"xfs\". Implicitly inferred to be \"ext4\" if unspecified.",
          "type": "string"

		return
	}
	z.NRRequests, err = dc.ReadUint64()
	if err != nil {
		err = msgp.WrapError(err, "NRRequests")
		return
	}
	z.FSType, err = dc.ReadString()
	if err != nil {
		err = msgp.WrapError(err, "FSType")
		return
	}
	z.RootDisk, err = dc.ReadBool()
	if err != nil {
		err = msgp.WrapError(err, "RootDisk")
		return
	}
	z.Healing, err = dc.ReadBool()