Reshape - 5 vs 13#
Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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.
- Reshape5 → Reshape13 +0 -1
Reshape5 → Reshape13
RENAMED
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@@ -1 +1 @@
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1
1
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Reshape the input tensor similar to numpy.reshape.
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2
2
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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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3
3
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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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4
4
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inferred from the size of the tensor and the remaining dimensions. A dimension
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5
5
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could also be 0, in which case the actual dimension value is unchanged (i.e. taken
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6
6
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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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7
7
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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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8
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**Inputs**
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* **data** (heterogeneous) - **T**:
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10
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An input tensor.
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11
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* **shape** (heterogeneous) - **tensor(int64)**:
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12
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Specified shape for output.
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13
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**Outputs**
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14
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* **reshaped** (heterogeneous) - **T**:
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15
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Reshaped data.
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16
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**Type Constraints**
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* **T** in (
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18
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-
tensor(bfloat16),
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19
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tensor(bool),
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20
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tensor(complex128),
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21
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tensor(complex64),
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22
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tensor(double),
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23
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tensor(float),
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24
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tensor(float16),
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25
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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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33
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tensor(uint8)
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):
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35
34
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Constrain input and output types to all tensor types.
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