text: ## @package workspace
  # Module caffe2.python.workspace
  
  
  
  
  import collections
  import contextlib
  from google.protobuf.message import Message
  from multiprocessing import Process
  import os
  from collections import defaultdict
  import logging
  import numpy as np
  from past.builtins import basestring
  import shutil
  import socket
  import tempfile
  
  from caffe2.proto import caffe2_pb2
  from caffe2.python import scope, utils
  from caffe2.python.lazy import TriggerLazyImport
  
  import caffe2.python._import_c_extension as C
  
  logger = logging.getLogger(__name__)
  
  Blobs = C.blobs
  ResetBlob = C.reset_blob
  CreateBlob = C.create_blob
  CurrentWorkspace = C.current_workspace
  DeserializeBlob = C.deserialize_blob
  GlobalInit = C.global_init
  HasBlob = C.has_blob
  RegisteredOperators = C.registered_operators
  SerializeBlob = C.serialize_blob
  SwitchWorkspace = C.switch_workspace
  RootFolder = C.root_folder
  Workspaces = C.workspaces
  BenchmarkNet = C.benchmark_net
  BenchmarkNetOnce = C.benchmark_net_once
  GetStats = C.get_stats
  CreateOfflineTensor = C.create_offline_tensor
  
  operator_tracebacks = defaultdict(dict)
  
  is_asan = C.is_asan
  has_cuda_support = C.has_cuda_support
  has_hip_support = C.has_hip_support
  has_gpu_support = C.has_gpu_support
  if has_cuda_support:
      GpuDeviceType = caffe2_pb2.CUDA
      NumCudaDevices = C.num_cuda_devices
      # This is a duplicate of NumCudaDevices. Remove
      # NumCudaDevices once replaced everywhere in the code
      NumGpuDevices = C.num_cuda_devices
      GetCUDAVersion = C.get_cuda_version
      GetCuDNNVersion = C.get_cudnn_version
  
      def GetGpuPeerAccessPattern():
          return np.asarray(C.get_cuda_peer_access_pattern())
  
      GetDeviceProperties = C.get_device_properties
      GetGPUMemoryInfo = C.get_gpu_memory_info
  else:
      NumCudaDevices = lambda: 0 # noqa
      GetCUDAVersion = lambda: 0 # noqa
      GetCuDNNVersion = lambda: 0 # noqa
  
  if has_hip_support:
      GpuDeviceType = caffe2_pb2.HIP
      NumGpuDevices = C.num_hip_devices
      GetHIPVersion = C.get_hip_version
  
      def GetGpuPeerAccessPattern():
          return np.asarray(C.get_hip_peer_access_pattern())
      GetDeviceProperties = C.get_device_properties
      GetGPUMemoryInfo = C.get_gpu_memory_info
  
  if not has_gpu_support:
      # setting cuda as the default GpuDeviceType as some tests
      # like core, scope tests use GpuDeviceType even without gpu support
      GpuDeviceType = caffe2_pb2.CUDA
      NumGpuDevices = lambda: 0 # noqa
      GetDeviceProperties = lambda x: None # noqa
      GetGpuPeerAccessPattern = lambda: np.array([]) # noqa
      GetGPUMemoryInfo = lambda: None # noqa
  
  IsNUMAEnabled = C.is_numa_enabled
  GetNumNUMANodes = C.get_num_numa_nodes
  GetBlobNUMANode = C.get_blob_numa_node
  GetBlobSizeBytes = C.get_blob_size_bytes
  
  
  def FillRandomNetworkInputs(net, input_dims, input_types):
      C.fill_random_network_inputs(net.Proto().SerializeToString(), input_dims, input_types)
  
  
  def _GetFreeFlaskPort():
      """Get a free flask port."""
      # We will prefer to use 5000. If not, we will then pick a random port.
      sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
      result = sock.connect_ex(('127.0.0.1', 5000))
      if result == 0:
          return 5000
      else:
          s = socket.socket()
          s.bind(('', 0))
          port = s.getsockname()[1]
          s.close()
          # Race condition: between the interval we close the socket and actually
          # start a mint process, another process might have occupied the port. We
          # don't do much here as this is mostly for convenience in research
          # rather than 24x7 service.
          return port
  
  def StartMint(root_folder=None, port=None):
      """Start a mint instance.
  
      TODO(Yangqing): this does not work well under ipython yet. According to
          https://github.com/ipython/ipython/issues/5862
      writing up some fix is a todo item.
      """
      from caffe2.python.mint import app
      if root_folder is None:
          # Get the root folder from the current workspace
          root_folder = C.root_folder()
      if port is None:
          port = _GetFreeFlaskPort()
      process = Process(
          target=app.main,
          args=(
              ['-p', str(port), '-r', root_folder],
          )
      )
      process.start()
      print('Mint running at http://{}:{}'.format(socket.getfqdn(), port))
      return process
  
  
  def StringifyProto(obj):
      """Stringify a protocol buffer object.
  
    Inputs:
      obj: a protocol buffer object, or a Pycaffe2 object that has a Proto()
          function.
    Outputs:
      string: the output protobuf string.
    Raises:
      AttributeError: if the passed in object does not have the right attribute.
    """
      if isinstance(obj, basestring):
          return obj
      else:
          if isinstance(obj, Message):
              # First, see if this object is a protocol buffer, which we can
              # simply serialize with the SerializeToString() call.
              return obj.SerializeToString()
          elif hasattr(obj, 'Proto'):
              return obj.Proto().SerializeToString()
          else:
              raise ValueError("Unexpected argument to StringifyProto of type " +
                               type(obj).__name__)
  
  
  def ResetWorkspace(root_folder=None):
      if root_folder is None:
          # Reset the workspace, but keep the current root folder setting.
          return C.reset_workspace(C.root_folder())
      else:
          if not os.path.exists(root_folder):
              os.makedirs(root_folder)
          return C.reset_workspace(root_folder)
  
  
  def CreateNet(net, overwrite=False, input_blobs=None):
      TriggerLazyImport()
      if input_blobs is None:
          input_blobs = []
      for input_blob in input_blobs:
          C.create_blob(input_blob)
      return CallWithExceptionIntercept(
          C.create_net,
          C.Workspace.current._last_failed_op_net_position,
          GetNetName(net),
          StringifyProto(net), overwrite,
      )
  
  
  def Predictor(init_net, predict_net):
      return C.Predictor(StringifyProto(init_net), StringifyProto(predict_net))
  
  
  def GetOperatorCost(operator, blobs):
      return C.get_operator_cost(StringifyProto(operator), blobs)
  
  
  def RunOperatorOnce(operator):
      return C.run_operator_once(StringifyProto(operator))
  
  
  def RunOperatorMultiple(operator, num_runs):
      return C.run_operator_multiple(StringifyProto(operator), num_runs)
  
  
  def RunOperatorsOnce(operators):
      for op in operators:
          success = RunOperatorOnce(op)
          if not success:
              return False
      return True
  
  
  def ClearGlobalNetObserver():
      return C.clear_global_net_observer()
  
  
  def CallWithExceptionIntercept(func, op_id_fetcher, net_name, *args, **kwargs):
      try:
          return func(*args, **kwargs)
      except Exception:
          op_id = op_id_fetcher()
          net_tracebacks = operator_tracebacks.get(net_name, None)
          logger.warning(
              'Original python traceback for operator `{}` in network '
              '`{}` in exception above (most recent call last):'.format(
                  op_id, net_name))
          if net_tracebacks and op_id in net_tracebacks:
              tb = net_tracebacks[op_id]
              for line in reversed(tb):
                  logger.warning('  File "{}", line {}, in {}'.format(
                      line[0], line[1], line[2]))
          raise
  
  
  def RunNetOnce(net):
      return CallWithExceptionIntercept(
          C.run_net_once,
          C.Workspace.current._last_failed_op_net_position,
          GetNetName(net),
          StringifyProto(net),
      )
  
  
  def RunNet(name, num_iter=1, allow_fail=False):
      """Runs a given net.
  
      Inputs:
        name: the name of the net, or a reference to the net.
        num_iter: number of iterations to run
        allow_fail: if True, does not assert on net exec failure but returns False
      Returns:
        True or an exception.
      """
      return CallWithExceptionIntercept(
          C.run_net,
          C.Workspace.current._last_failed_op_net_position,
          GetNetName(name),
          StringifyNetName(name), num_iter, allow_fail,
      )
  
  
  def RunPlan(plan_or_step):
      # TODO(jiayq): refactor core.py/workspace.py to avoid circular deps
      import caffe2.python.core as core
      if isinstance(plan_or_step, core.ExecutionStep):
          plan_or_step = core.Plan(plan_or_step)
      return C.run_plan(StringifyProto(plan_or_step))
  
  
  def RunPlanInBackground(plan_or_step):
      # TODO(jiayq): refactor core.py/workspace.py to avoid circular deps
      import caffe2.python.core as core
      if isinstance(plan_or_step, core.ExecutionStep):
          plan_or_step = core.Plan(plan_or_step)
      return C.run_plan_in_background(StringifyProto(plan_or_step))
  
  
  def InferShapesAndTypes(nets, blob_dimensions=None, nets_proto=False,
                          blob_types=None):
      """Infers the shapes and types for the specified nets.
  
      Inputs:
        nets: the list of nets
        blob_dimensions (optional): a dictionary of blobs and their dimensions.
            If not specified, the workspace blobs are used.
        nets_proto (optional): a boolean flag indicating whether the protobuffer
            representation is passed to the routine.
      Returns:
        A tuple of (shapes, types) dictionaries keyed by blob name.
      """
      if nets_proto:
          net_protos = [StringifyProto(n) for n in nets]
      else:
          net_protos = [StringifyProto(n.Proto()) for n in nets]
      if blob_dimensions is None:
          assert blob_types is None
          blobdesc_prototxt = C.infer_shapes_and_types_from_workspace(net_protos)
      elif blob_types is None:
          blobdesc_prototxt = C.infer_shapes_and_types_from_map(
              net_protos, blob_dimensions
          )
      else:
          blobdesc_prototxt = C.infer_shapes_and_types_from_map(
              net_protos, blob_dimensions, blob_types
          )
      blobdesc_proto = caffe2_pb2.TensorShapes()
      blobdesc_proto.ParseFromString(blobdesc_prototxt)
      shapes = {}
      types = {}
      for ts in blobdesc_proto.shapes:
          if not ts.unknown_shape:
              shapes[ts.name] = list(ts.dims)
              types[ts.name] = ts.data_type
  
      return (shapes, types)
  
  
  def _StringifyName(name, expected_type):
      if isinstance(name, basestring):
          return name
      assert type(name).__name__ == expected_type, \
          "Expected a string or %s" % expected_type
      return str(name)
  
  
  def StringifyBlobName(name):
      return _StringifyName(name, "BlobReference")
  
  
  def StringifyNetName(name):
      return _StringifyName(name, "Net")
  
  
  def GetNetName(net):
      if isinstance(net, basestring):
          return net
      if type(net).__name__ == "Net":
          return net.Name()
      if isinstance(net, caffe2_pb2.NetDef):
          return net.name
      raise Exception("Not a Net object: {}".format(str(net)))
  
  
  def FeedBlob(name, arr, device_option=None):
      """Feeds a blob into the workspace.
  
      Inputs:
        name: the name of the blob.
        arr: either a TensorProto object or a numpy array object to be fed into
            the workspace.
        device_option (optional): the device option to feed the data with.
      Returns:
        True or False, stating whether the feed is successful.
      """
      ws = C.Workspace.current
      return _Workspace_feed_blob(ws, name, arr, device_option)
  
  
  def FetchBlobs(names):
      """Fetches a list of blobs from the workspace.
  
      Inputs:
          names: list of names of blobs - strings or BlobReferences
      Returns:
          list of fetched blobs
      """
      return [FetchBlob(name) for name in names]
  
  
  def FetchBlob(name):
      """Fetches a blob from the workspace.
  
      Inputs:
        name: the name of the blob - a string or a BlobReference
      Returns:
        Fetched blob (numpy array or string) if successful
      """
      result = C.fetch_blob(StringifyBlobName(name))
      if isinstance(result, tuple):
          raise TypeError(
              "Use FetchInt8Blob to fetch Int8 Blob {}".format(
                  StringifyBlobName(name)
              )
          )
      return result
  
  
  def FetchTorch(name):
      ws = C.Workspace.current
      return ws.blobs[name].to_torch()
  
  
  Int8Tensor = collections.namedtuple(
      'Int8Tensor', ['data', 'scale', 'zero_point']
  )
  
  
  def FetchInt8Blob(name):
      """Fetches an Int8 blob from the workspace. It shared backend implementation
      with FetchBlob but it is recommended when fetching Int8 Blobs
  
      Inputs:
        name: the name of the Int8 blob - a string or a BlobReference
      Returns:
        data: int8 numpy array, data
        scale: float, fake quantization scale
        zero_point: int, fake quantization offset
      """
      result = C.fetch_blob(StringifyBlobName(name))
      assert isinstance(result, tuple), \
          'You are not fetching an Int8Blob {}. Please use FetchBlob'.format(
              StringifyBlobName(name))
      return Int8Tensor(*result)
  
  
  def FetchInt8BlobRealVal(name):
      """Fetches an Int8 blob from the workspace and return its real value representation.
  
      Inputs:
        name: the name of the Int8 blob - a string or a BlobReference
      Returns:
        real value representation of int8 numpy array
      """
      result = C.fetch_blob(StringifyBlobName(name))
      assert isinstance(result, tuple), \
          'You are not fetching an Int8Blob {}. Please use FetchBlob'.format(
              StringifyBlobName(name))
      int8_blob = Int8Tensor(*result)
      return (int8_blob.data.astype(np.int32) - int(int8_blob.zero_point)).astype(
          np.float32) * int8_blob.scale
  
  
  def _Workspace_fetch_int8_blob(ws, name):
      """Fetches an Int8 blob from the workspace. It shared backend implementation
      with FetchBlob but it is recommended when fetching Int8 Blobs
  
      Inputs:
        name: the name of the Int8 blob - a string or a BlobReference
      Returns:
        data: int8 numpy array, data
        scale: float, fake quantization scale
        zero_point: int, fake quantization offset
      """
      result = ws.fetch_blob(name)
      assert isinstance(result, tuple), \
          'You are not fetching an Int8Blob {}. Please use fetch_blob'.format(
              StringifyBlobName(name))
      return Int8Tensor(*result)
  
  
  C.Workspace.fetch_int8_blob = _Workspace_fetch_int8_blob
  
  
  def ApplyTransform(transform_key, net):
      """Apply a Transform to a NetDef protobuf object, and returns the new
      transformed NetDef.
  
      Inputs:
        transform_key: the name of the transform, as it is stored in the registry
        net: a NetDef protobuf object
      Returns:
        Transformed NetDef protobuf object.
      """
      transformed_net = caffe2_pb2.NetDef()
      transformed_str = C.apply_transform(
          str(transform_key).encode('utf-8'),
          net.SerializeToString(),
      )
      transformed_net.ParseFromString(transformed_str)
      return transformed_net
  
  
  def ApplyTransformIfFaster(transform_key, net, init_net, **kwargs):
      """Apply a Transform to a NetDef protobuf object, and returns the new
      transformed NetDef, only if it runs faster than the original.
  
      The runs are performed on the current active workspace (gWorkspace).
      You should initialize that workspace before making a call to this function.
  
      Inputs:
        transform_key: the name of the transform, as it is stored in the registry
        net: a NetDef protobuf object
        init_net: The net to initialize the workspace.
        warmup_runs (optional):
          Determines how many times the net is run before testing.
          Will be 5 by default.
        main_runs (optional):
          Determines how many times the net is run during testing.
          Will be 10 by default.
        improvement_threshold (optional):
          Determines the factor which the new net needs to be faster
          in order to replace the old. Will be 1.01 by default.
  
      Returns:
        Either a Transformed NetDef protobuf object, or the original netdef.
      """
  
      warmup_runs = kwargs['warmup_runs'] if 'warmup_runs' in kwargs else 5
      main_runs = kwargs['main_runs'] if 'main_runs' in kwargs else 10
      improvement_threshold = kwargs['improvement_threshold'] \
          if 'improvement_threshold' in kwargs else 1.01
  
      transformed_net = caffe2_pb2.NetDef()
      transformed_str = C.apply_transform_if_faster(
          str(transform_key).encode('utf-8'),
          net.SerializeToString(),
          init_net.SerializeToString(),
          warmup_runs,
          main_runs,
          float(improvement_threshold),
      )
      transformed_net.ParseFromString(transformed_str)
      return transformed_net
  
  
  def GetNameScope():
      """Return the current namescope string. To be used to fetch blobs"""
      return scope.CurrentNameScope()
  
  
  class _BlobDict(object):
      """Provides python dict compatible way to do fetching and feeding"""
  
      def __getitem__(self, key):
          return FetchBlob(key)
  
      def __setitem__(self, key, value):
          return FeedBlob(key, value)
  
      def __len__(self):
          return len(C.blobs())
  
      def __iter__(self):
          return C.blobs().__iter__()
  
      def __contains__(self, item):
          return C.has_blob(item)
  
  
  blobs = _BlobDict()
  
  
  ################################################################################
  # Utilities for immediate mode
  #
  # Caffe2's immediate mode implements the following behavior: between the two
  # function calls StartImmediate() and StopImmediate(), for any operator that is
  # called through CreateOperator(), we will also run that operator in a workspace
  # that is specific to the immediate mode. The user is explicitly expected to
  # make sure that these ops have proper inputs and outputs, i.e. one should not
  # run an op where an external input is not created or fed.
  #
  # Users can use FeedImmediate() and FetchImmediate() to interact with blobs
  # in the immediate workspace.
  #
  # Once StopImmediate() is called, all contents in the immediate workspace is
  # freed up so one can continue using normal runs.
  #
  # The immediate mode is solely for debugging purposes and support will be very
  # sparse.
  ################################################################################
  
  _immediate_mode = False
  _immediate_workspace_name = "_CAFFE2_IMMEDIATE"
  _immediate_root_folder = ''
  
  
  def IsImmediate():
      return _immediate_mode
  
  
  @contextlib.contextmanager
  def WorkspaceGuard(workspace_name):
      current = CurrentWorkspace()
      SwitchWorkspace(workspace_name, True)
      yield
      SwitchWorkspace(current)
  
  
  def StartImmediate(i_know=False):
      global _immediate_mode
      global _immediate_root_folder
      if IsImmediate():
          # already in immediate mode. We will kill the previous one
          # and start from fresh.
          StopImmediate()
      _immediate_mode = True
      with WorkspaceGuard(_immediate_workspace_name):
          _immediate_root_folder = tempfile.mkdtemp()
          ResetWorkspace(_immediate_root_folder)
      if i_know:
          # if the user doesn't want to see the warning message, sure...
          return
      print("""
      Enabling immediate mode in caffe2 python is an EXTREMELY EXPERIMENTAL
      feature and may very easily go wrong. This is because Caffe2 uses a
      declarative way of defining operators and models, which is essentially
      not meant to run things in an interactive way. Read the following carefully
      to make sure that you understand the caveats.
  
      (1) You need to make sure that the sequences of operators you create are
      actually runnable sequentially. For example, if you create an op that takes
      an input X, somewhere earlier you should have already created X.
  
      (2) Caffe2 immediate uses one single workspace, so if the set of operators
      you run are intended to be under different workspaces, they will not run.
      To create boundaries between such use cases, you can call FinishImmediate()
      and StartImmediate() manually to flush out everything no longer needed.
  
      (3) Underlying objects held by the immediate mode may interfere with your
      normal run. For example, if there is a leveldb that you opened in immediate
      mode and did not close, your main run will fail because leveldb does not
      support double opening. Immediate mode may also occupy a lot of memory esp.
      on GPUs. Call FinishImmediate() as soon as possible when you no longer
      need it.
  
      (4) Immediate is designed to be slow. Every immediate call implicitly
      creates a temp operator object, runs it, and destroys the operator. This
      slow-speed run is by design to discourage abuse. For most use cases other
      than debugging, do NOT turn on immediate mode.
  
      (5) If there is anything FATAL happening in the underlying C++ code, the
      immediate mode will immediately (pun intended) cause the runtime to crash.
  
      Thus you should use immediate mode with extra care. If you still would
      like to, have fun [https://xkcd.com/149/].
      """)
  
  
  def StopImmediate():
      """Stops an immediate mode run."""
      # Phew, that was a dangerous ride.
      global _immediate_mode
      global _immediate_root_folder
      if not IsImmediate():
          return
      with WorkspaceGuard(_immediate_workspace_name):
          ResetWorkspace()
      shutil.rmtree(_immediate_root_folder)
      _immediate_root_folder = ''
      _immediate_mode = False
  
  
  def ImmediateBlobs():
      with WorkspaceGuard(_immediate_workspace_name):
          return Blobs()
  
  
  def RunOperatorImmediate(op):
      with WorkspaceGuard(_immediate_workspace_name):
          RunOperatorOnce(op)
  
  
  def FetchImmediate(*args, **kwargs):
      with WorkspaceGuard(_immediate_workspace_name):
          return FetchBlob(*args, **kwargs)
  
  
  def FeedImmediate(*args, **kwargs):
      with WorkspaceGuard(_immediate_workspace_name):
          return FeedBlob(*args, **kwargs)
  
  
  # C.Workspace methods.
  
  def _Workspace_create_net_with_exception_intercept(ws, net, overwrite=False):
      return CallWithExceptionIntercept(
          ws._create_net,
          ws._last_failed_op_net_position,
          GetNetName(net),
          StringifyProto(net), overwrite,
      )
  
  
  def _Workspace_run(ws, obj):
      if hasattr(obj, 'Proto'):
          obj = obj.Proto()
      if isinstance(obj, caffe2_pb2.PlanDef):
          return ws._run_plan(obj.SerializeToString())
      if isinstance(obj, caffe2_pb2.NetDef):
          return CallWithExceptionIntercept(
              ws._run_net,
              ws._last_failed_op_net_position,
              GetNetName(obj),
              obj.SerializeToString(),
          )
          # return ws._run_net(obj.SerializeToString())
      if isinstance(obj, caffe2_pb2.OperatorDef):
          return ws._run_operator(obj.SerializeToString())
      raise ValueError(
          "Don't know how to do Workspace.run() on {}".format(type(obj)))
  
  
  def _Workspace_feed_blob(ws, name, arr, device_option=None):
      if type(arr) is caffe2_pb2.TensorProto:
          arr = utils.Caffe2TensorToNumpyArray(arr)
      if type(arr) is np.ndarray and arr.dtype.kind in 'SU':
          # Plain NumPy strings are weird, let's use objects instead
          arr = arr.astype(np.object)
  
      if device_option is None:
          device_option = scope.CurrentDeviceScope()
  
      if device_option and device_option.device_type == caffe2_pb2.CUDA:
          if arr.dtype == np.dtype('float64'):
              logger.warning(
                  "CUDA operators do not support 64-bit doubles, " +
                  "please use arr.astype(np.float32) or np.int32 for ints." +
                  " Blob: {}".format(name) +
                  " type: {}".format(str(arr.dtype))
              )
  
      name = StringifyBlobName(name)
      if device_option is not None:
          return ws.create_blob(name).feed(arr, device_option)
      else:
          return ws.create_blob(name).feed(arr)
  
  
  def _Workspace_remove_blob(ws, blob):
      ws._remove_blob(str(blob))
  
  
  Workspace = C.Workspace
  Workspace.create_net = _Workspace_create_net_with_exception_intercept
  Workspace.run = _Workspace_run
  Workspace.feed_blob = _Workspace_feed_blob
  Workspace.remove_blob = _Workspace_remove_blob
  
  # C.Blob methods.
  
  
  def _Blob_feed(blob, arg, device_option=None):
      # conservative type check to avoid unnecessary import
      if type(arg).__name__ == 'Tensor' and type(arg).__module__ == 'torch':
          import torch
          if isinstance(arg, torch.Tensor):
              assert device_option is None, \
                  "device_option doesn't make sense with PyTorch tensors"
              handle = torch._C._tensor_impl_raw_handle(arg)
              blob._wrap_tensor_impl(handle)
              return True  # _feed() returns True for some reason
      if device_option is not None:
          device_option = StringifyProto(device_option)
      return blob._feed(arg, device_option)
  
  
  C.Blob.feed = _Blob_feed
  
  
  def _Tensor_to_torch(tensor):
      """
      PyTorch tensor interop (TensorCPU methods)
  
      Can be accessed as:
        workspace.Workspace.current.blobs['foo'].tensor().to_torch()
      """
      # avoiding circular dependency
      import torch
      handle = tensor._tensor_impl_raw_handle()
      return torch._C._wrap_tensor_impl(handle)
  
  C.TensorCPU.to_torch = _Tensor_to_torch
  
  
  def _Blob_to_torch(blob):
      if not blob.is_tensor():
          raise RuntimeError("Blob has to be a tensor")
      return blob.as_tensor().to_torch()
  
  C.Blob.to_torch = _Blob_to_torch
  