module `onnxrt.ops_cpu.op_range`#

Short summary#

module mlprodict.onnxrt.ops_cpu.op_range

Runtime operator.

class	truncated documentation
`Range`	Range ===== Generate a tensor containing a sequence of numbers that begin at start and extends by increments of delta …

property	truncated documentation
`args_default`	Returns the list of arguments as well as the list of parameters with the default values (close to the signature). …
`args_default_modified`	Returns the list of modified parameters.
`args_mandatory`	Returns the list of optional arguments.
`args_optional`	Returns the list of optional arguments.
`atts_value`	Returns all parameters in a dictionary.

method	truncated documentation
`__init__`
`_run`

Runtime operator.

class mlprodict.onnxrt.ops_cpu.op_range.Range(onnx_node, desc=None, **options)#

Generate a tensor containing a sequence of numbers that begin at start and extends by increments of delta up to limit (exclusive).

The number of elements in the output of range is computed as below-

number_of_elements = max( ceil( (limit - start) / delta ) , 0 )

The pseudocode determining the contents of the output is shown below-

for(int i=0; i<number_of_elements; ++i)

{

` output[i] = start + (i * delta); `

}

Example 1 Inputs: start = 3, limit = 9, delta = 3 Output: [3, 6]

Example 2 Inputs: start = 10, limit = 4, delta = -2 Output: [10, 8, 6]

Inputs

start (heterogeneous)T: Scalar. First entry for the range of output values.
limit (heterogeneous)T: Scalar. Exclusive upper limit for the range of output values.
delta (heterogeneous)T: Scalar. Value to step by.

Outputs

output (heterogeneous)T: A 1-D tensor with same type as the inputs containing generated range of values.

Type Constraints

T tensor(float), tensor(double), tensor(int16), tensor(int32), tensor(int64): Constrain input types to common numeric type tensors.

Version

Onnx name: Range

This version of the operator has been available since version 11.

Runtime implementation: Range

_run(starts, ends, steps, attributes=None, verbose=0, fLOG=None)#

Should be overwritten.