![]() Enter the amount to repay and select either “reduce term” or “reduce monthly repayment”.Ħ. float32)Īxis = 1 keepdims = 0 node = onnx. Type Constraints T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16) Constrain input and output types to all numeric tensors. ![]() Outputs reduced (non-differentiable) : tensor(int64) Reduced output tensor with integer data type. Inputs data (non-differentiable) : T An input tensor. select_last_index : int (default is 0) Whether to select the last index or the first index if the appears in multiple indices, default is False (first index). keepdims : int (default is 1) Keep the reduced dimension or not, default 1 means keep reduced dimension. Other versions of this operator: 1, 11, 12 Attributes axis : int (default is 0) The axis in which to compute the arg indices. The type of the output tensor is integer. Is selected if the max appears more than once in the input. If select_last_index is True (default False), the index of the last occurrence of the max ![]() If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. The resulting tensor has the same rank as the input if keepdims equals 1. astype( bool)Įxpect( node, inputs =, outputs =, name = "test_and_bcast4v4d") ArgMaxĬomputes the indices of the max elements of the input tensor's element along the astype( bool)Įxpect( node, inputs =, outputs =, name = "test_and_bcast4v3d") astype( bool)Įxpect( node, inputs =, outputs =, name = "test_and_bcast4v2d") astype( bool)Įxpect( node, inputs =, outputs =, name = "test_and_bcast3v2d") astype( bool)Įxpect( node, inputs =, outputs =, name = "test_and_bcast3v1d") T1 : tensor(bool) Constrain output to boolean tensor. Type Constraints T : tensor(bool) Constrain input to boolean tensor. Outputs C (non-differentiable) : T1 Result tensor. B (non-differentiable) : T Second input operand for the logical operator. ![]() Other versions of this operator: 1 Inputs A (non-differentiable) : T First input operand for the logical operator. This version of the operator has been available since version 7 of the default ONNX operator set. This operator supports multidirectional (i.e., Numpy-style) broadcasting for more details please check the doc. Returns the tensor resulted from performing the and logical operationĮlementwise on the input tensors A and B (with Numpy-style broadcasting support). uint8)Įxpect( node, inputs =, outputs =, name = "test_add_uint8") And Outputs C (differentiable) : T Result, has same element type as two inputs Type Constraints T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16) Constrain input and output types to all numeric tensors. Other versions of this operator: 1, 6, 7, 13 Inputs A (differentiable) : T First operand. This version of the operator has been available since version 14 of the default ONNX operator set. (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16. Performs element-wise binary addition (with Numpy-style broadcasting support). float32)Įxpect( node, inputs =, outputs =, name = "test_acosh") Add arccosh( x) # expected output expect( node, inputs =, outputs =, name = "test_acosh_example") Other versions of this operator: 1, 6 Inputs X (differentiable) : T Input tensor Outputs Y (differentiable) : T Output tensor Type Constraints T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16) Constrain input and output types to all numeric tensors. This version of the operator has been available since version 13 of the default ONNX operator set. (Tensor) where absolute value, y = abs(x), is applied to ai.onnx (default) OperatorĪbsolute takes one input data (Tensor) and produces one output data Is not specified, that variable has undefined differentiability. This file is automatically generated from theĭo not modify directly and instead edit operator definitions.įor an operator input/output's differentiability, it can be differentiable,
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