.. _l-onnx-op-shape-d13-19:

Shape - 13 vs 19
================

Next section compares an older to a newer version of the same operator 
after both definition are converted into markdown text.
Green means an addition to the newer version, red means a deletion.
Anything else is unchanged.

.. raw:: html


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    --- a/Shape13
    +++ b/Shape19
    @@ -1 +1 @@
      Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor.
    + Optional attributes start and end can be used to compute a slice of the input tensor's shape.
    + If start axis is omitted, the slice starts from axis 0.
    + The end axis, if specified, is exclusive (and the returned value will not include the size of that axis).
    + If the end axis is omitted, the axes upto the last one will be included.
    + Negative axes indicate counting back from the last axis.
    + Note that axes will be clamped to the range [0, r-1], where r is the
    + rank of the input tensor if they are out-of-range (after adding r in the case of
    + negative axis). Thus, specifying any end value > r is equivalent to specifying an end
    + value of r, and specifying any start value < -r is equivalent to specifying a start
    + value of 0.
    + 
    + Examples:
    + 
    + 
    + Input tensor with shape: [2, 3, 4]
    + No attributes specified.
    + Output: [2, 3, 4]
    + 
    + 
    + 
    + Input tensor with shape: [2, 3, 4]
    + start: -1
    + Output: [4]
    + 
    + 
    + 
    + Input tensor with shape: [2, 3, 4]
    + end: -1
    + Output: [2, 3]
    + 
    + 
    + 
    + Input tensor with shape: [2, 3, 4]
    + start: 1
    + end: 2
    + Output: [3]
    + 
    + 
    + ### Attributes
    + 
    + * **end - INT** :
    + 
    +   (Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.
    + 
    + * **start - INT** (default is '0'):
    + 
    +   (Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.
  
      ### Inputs
  
      - **data** (heterogeneous) - **T**:
  
        An input tensor.
  
      ### Outputs
  
      - **shape** (heterogeneous) - **T1**:
  
        Shape of the input tensor
  
      ### Type Constraints
  
    - * **T** in ( tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):
    + * **T** in ( tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):
    ?                                                                                                                                  ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  
        Input tensor can be of arbitrary type.
      * **T1** in ( tensor(int64) ):
  
        Constrain output to int64 tensor.
    `;

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