code: def tensor_to_funsor(
      tensor, event_inputs=(), event_output=0, dtype="real"
  ):
      """
      Convert a :class:`torch.Tensor` to a :class:`funsor.tensor.Tensor` .
  
      Note this should not touch data, but may trigger a
      :meth:`torch.Tensor.reshape` op.
  
      :param torch.Tensor tensor: A PyTorch tensor.
      :param tuple event_inputs: A tuple of names for rightmost tensor
          dimensions.  If ``tensor`` has these names, they will be converted to
          ``result.inputs``.
      :param int event_output: The number of tensor dimensions assigned to
          ``result.output``. These must be on the right of any ``event_input``
          dimensions.
      :return: A funsor.
      :rtype: funsor.tensor.Tensor
      """
      assert isinstance(tensor, torch.Tensor)
      assert isinstance(event_inputs, tuple)
      assert isinstance(event_output, int) and event_output >= 0
      inputs_shape = tensor.shape[: tensor.dim() - event_output]
      output = Array[dtype, tensor.shape[tensor.dim() - event_output :]]
      dim_to_name = default_dim_to_name(inputs_shape, event_inputs)
      return to_funsor(tensor, output, dim_to_name)
  
imports: import math
  from collections import OrderedDict
  import torch
  import funsor.ops
  from funsor.cnf import Contraction
  from funsor.delta import Delta
  from funsor.domains import Array, Bint, Real, Reals
  from funsor.gaussian import Gaussian
  from funsor.interpreter import gensym
  from funsor.tensor import Tensor
  from funsor.terms import Independent, Variable, to_data, to_funsor
label_desc: Use image node_id 3 for calling a global function with example usage: tensor_to_funsor(tensor, event_inputs, event_output, dtype) and returns: to_funsor