class_docstr: Slicing input tensor with given ``size`` for each rank.
  
  Parameters
  ----------
  x: tensor<*?, T> (Required)
      * Input tensor.
  begin: tensor<[rank(x)], i32> Required
      * The begin index for slice.
  size: tensor<[rank(x)], i32> Required
      * The size that is to be sliced. If ``size`` is ``-1``, we slice all the remaining elements.
  
  Returns
  -------
  tensor<*?, T>
      * Scalar or tensor. Same type as the input tensor.
  
  Attributes
  ----------
  T: fp32
code: def __init__(self, **kwargs):
      super(slice_by_size, self).__init__(**kwargs)
  
imports: import functools
  import sympy
  from coremltools.converters.mil.mil.types.symbolic import is_symbolic, isscalar, any_symbolic, any_variadic
  from coremltools.converters.mil.mil import get_new_symbol, get_new_variadic_symbol, VALUE, SYMBOL, types
  from ._op_reqs import *
label_desc: Use image node_id 1 to create a new slice_by_size object from inherited base classes: Operation with example: obj = slice_by_size()