Tile - 1 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Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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.
- Tile1 → Tile13 +9 -10
Tile1 → Tile13
RENAMED
@@ -1 +1 @@
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1
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-
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1
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Constructs a tensor by tiling a given tensor.
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2
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This is the same as function tile in Numpy, but no broadcast.
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3
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For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]]
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2
4
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### Inputs
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3
5
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- **input** (heterogeneous) - **T**:
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4
6
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Input tensor of any shape.
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5
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- **
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7
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- **repeats** (heterogeneous) - **T1**:
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8
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1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.
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6
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Number of repeated copies to make of the input tensor.
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7
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- **axis** (heterogeneous) - **T**:
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8
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-
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9
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Axis along which to repeat.
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10
9
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### Outputs
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11
10
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- **output** (heterogeneous) - **T**:
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12
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Output tensor of same
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11
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Output tensor of the same dimensions and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]
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13
12
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### Type Constraints
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14
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* **T** in ( tensor(double), tensor(float), tensor(float16) ):
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13
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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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15
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Constrain input types to
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14
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Constrain input and output types to all tensor types.
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15
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* **T1** in ( tensor(int64) ):
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17
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Constrain
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16
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Constrain repeat's type to int64 tensors.? +++++
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