parameters: (self, x, sample=True, batch=True, event=True)
return_statement: return sample_shape + batch_shape + event_shape
docstring: Returns subset of tensor's dimension indexes (indexes into shape).
  
      Args:
        x: `Tensor`.
        sample: `Boolean`. Include sample dimensions or not.
        batch: `Boolean`. Include batch dimensions or not.
        event: `Boolean`. Include event dimensions or not.
  
      Raises:
        ValueError: if `x.get_shape().ndims` is `None`
  
      Returns:
        List enumerating requested dimensions.
docstring_summary: Returns subset of tensor's dimension indexes (indexes into shape).
docstring_tokens: Returns subset of tensor s dimension indexes ( indexes into shape ) .
function: def get_dims(self, x, sample=True, batch=True, event=True):
      """Returns subset of tensor's dimension indexes (indexes into shape).
  
      Args:
        x: `Tensor`.
        sample: `Boolean`. Include sample dimensions or not.
        batch: `Boolean`. Include batch dimensions or not.
        event: `Boolean`. Include event dimensions or not.
  
      Raises:
        ValueError: if `x.get_shape().ndims` is `None`
  
      Returns:
        List enumerating requested dimensions.
      """
      ndims = self.get_ndims(x)
  
      if sample and batch and event:
        return list(range(ndims))
  
      sample_start = 0
      batch_start = self.get_sample_ndims(x)
      event_start = batch_start + self.batch_ndims
  
      sample_shape = list(range(sample_start, batch_start)) if sample else []
      batch_shape = list(range(batch_start, event_start)) if batch else []
      event_shape = list(range(event_start, ndims)) if event else []
  
      return sample_shape + batch_shape + event_shape
function_tokens: def get_dims ( self , x , sample = True , batch = True , event = True ) : ndims = self . get_ndims ( x ) if sample and batch and event : return list ( range ( ndims ) ) sample_start = 0 batch_start = self . get_sample_ndims ( x ) event_start = batch_start + self . batch_ndims sample_shape = list ( range ( sample_start , batch_start ) ) if sample else [ ] batch_shape = list ( range ( batch_start , event_start ) ) if batch else [ ] event_shape = list ( range ( event_start , ndims ) ) if event else [ ] return sample_shape + batch_shape + event_shape