code: def batch_multiply(float_or_vector, tensor):
      if isinstance(float_or_vector, torch.Tensor):
          assert len(float_or_vector) == len(tensor)
          tensor = _batch_multiply_tensor_by_vector(
              float_or_vector, tensor
          )
      elif isinstance(float_or_vector, float):
          tensor *= float_or_vector
      else:
          raise TypeError("Value has to be float or torch.Tensor")
      return tensor
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 14 for calling a global function with example usage: batch_multiply(float_or_vector, tensor) and returns: tensor