**Returns:** Promise<{{MLComputeResult}}>.

const constant = builder.constant(operandType.dataType(), 0.2);

Issue(whatwg/webidl#1388): Support for unions of {{bigint}} and [=numeric types=] new in [[WEBIDL]], and implementation support is also limited. Prototype implementations are encouraged to provide feedback for this approach.

Return a data type of the {{MLOperand}}.

Return a shape of the {{MLOperand}}.

**Returns:** a sequence of {{unsigned long}}. The shape of the operand.

**Returns:** an {{MLOperand}} object.

The {{MLGraphBuilder}} API allows creating an {{MLGraph}} without input operands. If the underlying platform doesn't support that, implementations may add a stub input or passing constants as inputs to the graph.

- <dfn>inputs</dfn>: a sequence of {{MLOperand}}. All input tensors must have the

- <dfn>newShape</dfn>: a sequence of {{unsigned long}}. The new shape the input tensor is expanded to.

**Returns:** a sequence of {{MLOperand}}. The first element of the sequence is a 3-D tensor of shape *[numDirections, batchSize, hiddenSize]*, the cell output from the last time step of the network. Additionally, if |options|.{{MLGruOptions/returnSequence}} is set to true, the second element is the 4-D output tensor of shape *[steps, numDirections, batchSize, hiddenSize]* containing every cell outputs from each time step in the temporal sequence.

**Returns:** a sequence of {{MLOperand}}. The first element of the sequence is a 3-D tensor of shape *[numDirections, batchSize, hiddenSize]*, the output hidden state from the last time step of the network. The second element is a 3-D tensor of shape *[numDirections, batchSize, hiddenSize]*, the output cell state from the last time step of the network. Additionally, if |options|.{{MLLstmOptions/returnSequence}} is set to true, the third element is the 4-D output tensor of shape *[steps, numDirections, batchSize, hiddenSize]* containing every output from each time step in the temporal sequence.

**Returns:** a sequence of {{MLOperand}}. The first element of the sequence is the output hidden state of the current time step of the recurrent network. The following element is the output cell state. Both elements are 2-D tensors of shape *[batchSize, hiddenSize]*.

- <dfn>beginningPadding</dfn>: a sequence of {{unsigned long}}. The sequence of unsigned integer values indicating the number of padding values to add at the beginning of each input dimension, of length *N* where *N* is the [=MLOperand/rank=] of the input tensor. For each dimension *d* of *input*, *beginningPadding[d]* indicates how many values to add before the content in that dimension.

- <dfn>endingPadding</dfn>: a sequence of {{unsigned long}}. The sequence of unsigned integer values indicating the number of padding values to add at the ending of each input dimension, of length *N* where *N* is the [=MLOperand/rank=] of the input tensor. For each dimension *d* of *input*, *endingPadding[d]* indicates how many values to add after the content in that dimension.

Specifies the two consecutive dimensions of the input tensor to which the interpolation algorithm applies. The valid values in the sequence are [0, 1], [1, 2] or [2, 3].

- <dfn>newShape</dfn>: a sequence of {{unsigned long}}. The shape of the output tensor.

- <dfn>starts</dfn>: a sequence of {{unsigned long}}. The sequence of unsigned integer values indicating the starting index to slice of each input dimension, of length N where N is the [=MLOperand/rank=] of the input tensor. For each dimension *d* of *input*, *starts[d]* indicates the starting index to slice in that dimension. The starting index must be in the range [0, input size - 1] in that dimension.

- <dfn>sizes</dfn>: a sequence of {{unsigned long}}. The sequence of unsigned integer values indicating the number of elements to slice of each input dimension, of length N where N is the [=MLOperand/rank=] of the input tensor. For each dimension *d* of *input*, *sizes[d]* indicates the number of elements to slice in that dimension. The size must not be 0 and must satisfy the constraint `starting index + size <= input size` in that dimension.

- <dfn>splits</dfn>: an {{unsigned long}} or a sequence of {{unsigned long}}. If an {{unsigned long}}, it specifies the number of output tensors along the axis. The number must evenly divide the dimension size of *input* along *options.axis*. If a sequence of {{unsigned long}}, it specifies the sizes of each output tensor along the *options.axis*. The sum of sizes must equal to the dimension size of *input* along *options.axis*.

**Returns:** a sequence of {{MLOperand}}. The splitted output tensors. If *splits* is an {{unsigned long}}, the [=list/size=] of the output sequence equals to *splits*. The shape of each output tensor is the same as *input* except the dimension size of *axis* equals to the quotient of dividing the dimension size of *input* along *axis* by *splits*. If *splits* is a sequence of {{unsigned long}}, the [=list/size=] of the output sequence equals to the [=list/size=] of *splits*. The shape of the i-th output tensor is the same as *input* except along *axis* where the dimension size is *splits[i]*.

1. If |splits| is a sequence of {{unsigned long}}:

The default value is [N-1, ..., 0], where N is the [=MLOperand/rank=] of the input tensor, e.g. [2,1,0] for a 3-D tensor.

These default values cause the output to become a transposed tensor of the input. When specified, the number of values in the sequence must be the same as the [=MLOperand/rank=] of the input tensor, and the values in the sequence must be within the range from 0 to N-1 with no two or more same values found in the sequence.