Reshape - 1 vs 14

Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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 → Reshape14 +15 -10
Reshape1 → Reshape14 RENAMED
@@ -1 +1 @@
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  Reshape the input tensor similar to numpy.reshape.
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- It takes a tensor as input and an argument shape. It outputs the reshaped tensor.
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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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  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). If 'allowzero' is set, and the new shape includes 0, the
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+ dimension will be set explicitly to zero (i.e. not taken from input tensor).
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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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+ 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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+
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+ If the attribute 'allowzero' is set, it is invalid for the specified shape to
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+ contain both a zero value and -1, as the value of the dimension corresponding
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+ to -1 cannot be determined uniquely.
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  ### Attributes
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- * **consumed_inputs - INTS** :
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+ * **allowzero - INT** (default is '0'):
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+ (Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.
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- legacy optimization attribute.
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-
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- * **shape - INTS** :
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-
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- New shape
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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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+
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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 ( tensor(double), tensor(float), tensor(float16) ):
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+ * **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) ):
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- Constrain input and output types to float tensors.? ^ ^^^
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+ Constrain input and output types to all tensor types.? ^ ^ +++++