0  0  The operator computes the hardmax (1 for the first maximum value, and 0 for all others) values for each layer in the batch  The operator computes the hardmax (1 for the first maximum value, and 0 for all others) values for each layer in the batch 
1  1  of the given input. The input is a 2D tensor (Tensor<float>) of size  of the given input.

2   (batch_size x input_feature_dimensions). The output tensor has the same shape  
3   and contains the hardmax values of the corresponding input.  
4  2 


5  3  Input does not need to explicitly be a 2D vector; rather, it will be  The input does not need to explicitly be a 2D vector; rather, it will be

6  4  coerced into one. For an arbitrary ndimensional tensor  coerced into one. For an arbitrary ndimensional tensor 
7  5  input \in [a_0, a_1, ..., a_{k1}, a_k, ..., a_{n1}] and k is  input \in [a_0, a_1, ..., a_{k1}, a_k, ..., a_{n1}] and k is 
8  6  the axis provided, then input will be coerced into a 2dimensional tensor with  the axis provided, then input will be coerced into a 2dimensional tensor with 
9  7  dimensions [a_0 * ... * a_{k1}, a_k * ... * a_{n1}]. For the default  dimensions [a_0 * ... * a_{k1}, a_k * ... * a_{n1}]. For the default 
10  8  case where axis=1, this means the input tensor will be coerced into a 2D tensor  case where axis=1, this means the input tensor will be coerced into a 2D tensor 
11  9  of dimensions [a_0, a_1 * ... * a_{n1}], where a_0 is often the batch size.  of dimensions [a_0, a_1 * ... * a_{n1}], where a_0 is often the batch size. 
12  10  In this situation, we must have a_0 = N and a_1 * ... * a_{n1} = D.  In this situation, we must have a_0 = N and a_1 * ... * a_{n1} = D. 
13  11  Each of these dimensions must be matched correctly, or else the operator  Each of these dimensions must be matched correctly, or else the operator 
14  12  will throw errors.  will throw errors. The output tensor has the same shape

 13   and contains the hardmax values of the corresponding input. 
15  14 


16  15  **Attributes**  **Attributes** 
17  16 


18  17  * **axis**:  * **axis**: 
19  18  Describes the axis of the inputs when coerced to 2D; defaults to one  Describes the axis of the inputs when coerced to 2D; defaults to one 
20  19  because the 0th axis most likely describes the batch_size Default value is 1.  because the 0th axis most likely describes the batch_size. Negative

 20   value means counting dimensions from the back. Accepted range is 
 21   [r, r1] where r = rank(input). Default value is 1. 
21  22 


22  23  **Inputs**  **Inputs** 
23  24 


24  25  * **input** (heterogeneous)  **T**:  * **input** (heterogeneous)  **T**: 
25  26  The input tensor that's coerced into a 2D matrix of size (NxD) as  The input tensor that's coerced into a 2D matrix of size (NxD) as 
26  27  described above.  described above. 
27  28 


28  29  **Outputs**  **Outputs** 
29  30 


30  31  * **output** (heterogeneous)  **T**:  * **output** (heterogeneous)  **T**: 
31  32  The output values with the same shape as input tensor (the original  The output values with the same shape as input tensor (the original 
32  33  size without coercion).  size without coercion). 
33  34 


34  35  **Type Constraints**  **Type Constraints** 
35  36 


36  37  * **T** in (  * **T** in ( 
37  38  tensor(double),  tensor(double), 
38  39  tensor(float),  tensor(float), 
39  40  tensor(float16)  tensor(float16) 
40  41  ):  ): 
41  42  Constrain input and output types to float tensors.  Constrain input and output types to float tensors. 