Reshape - 1 vs 5#

Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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.

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  1. Reshape1 → Reshape5 +10 -17
Reshape1 → Reshape5 RENAMED
@@ -1 +1 @@
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  Reshape the input tensor similar to numpy.reshape.
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- First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
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+ It takes a tensor as input and an argument shape. It outputs the reshaped tensor.
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  At most one dimension of the new shape can be -1. In this case, the value is
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  inferred from the size of the tensor and the remaining dimensions. A dimension
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  could also be 0, in which case the actual dimension value is unchanged (i.e. taken
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  from the input tensor). Shape (second input) could be an empty shape, which means converting to a scalar.
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  The input tensor's shape and the output tensor's shape are required to have the same number of elements.
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+ **Attributes**
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+
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+ * **consumed_inputs**:
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+ legacy optimization attribute.
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+ * **shape**:
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+ New shape
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+
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  **Inputs**
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  * **data** (heterogeneous) - **T**:
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  An input tensor.
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- * **shape** (heterogeneous) - **tensor(int64)**:
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- Specified shape for output.
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  **Outputs**
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  * **reshaped** (heterogeneous) - **T**:
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  Reshaped data.
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  **Type Constraints**
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  * **T** in (
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- tensor(bool),
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- tensor(complex128),
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- tensor(complex64),
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  tensor(double),
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  tensor(float),
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- tensor(float16),
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+ tensor(float16)
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- tensor(int16),
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- tensor(int32),
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- tensor(int64),
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- tensor(int8),
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- tensor(string),
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- tensor(uint16),
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- tensor(uint32),
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- tensor(uint64),
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- tensor(uint8)
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  ):
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- Constrain input and output types to all tensor types.? ^^ -----
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+ Constrain input and output types to float tensors.? +++ ^