class_docstr: This is the main class which would represent neural type concept.
  It is used to represent *the types* of inputs and outputs.
  
  Args:
      axes (Optional[Tuple]): a tuple of AxisTypes objects representing the semantics of what varying each axis means
          You can use a short, string-based form here. For example: ('B', 'C', 'H', 'W') would correspond to an NCHW
          format frequently used in computer vision. ('B', 'T', 'D') is frequently used for signal processing and
          means [batch, time, dimension/channel].
      elements_type (ElementType): an instance of ElementType class representing the semantics of what is stored
          inside the tensor. For example: logits (LogitsType), log probabilities (LogprobType), etc.
      optional (bool): By default, this is false. If set to True, it would means that input to the port of this
          type can be optional.
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code: def __repr__(self):
      if torch.jit.is_scripting():
          return "SuppressedForTorchScript"
  
      if self.axes is not None:
          axes = str(self.axes)
      else:
          axes = "None"
  
      if self.elements_type is not None:
          element_type = repr(self.elements_type)
      else:
          element_type = "None"
  
      data = f"axis={axes}, element_type={element_type}"
  
      if self.optional:
          data = f"{data}, optional={self.optional}"
  
      final = f"{self.__class__.__name__}({data})"
      return final
  
imports: from typing import Any, Optional
  import torch
  from nemo.core.neural_types.axes import AxisKind, AxisType
  from nemo.core.neural_types.comparison import NeuralTypeComparisonResult
  from nemo.core.neural_types.elements import ElementType, VoidType
label_desc: Use image node_id 8 for calling the NeuralType obj's underlying member method code with example usage: obj.__repr__() and returns: final, str