function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {
    (0, _utilBase.assertNonNegativeIntegerDimensions)(outputShape);
    const $sparseIndices = (0, _tensorUtilEnv.convertToTensor)(sparseIndices, "sparseIndices", "sparseToDense", "int32");
    const $sparseValues = (0, _tensorUtilEnv.convertToTensor)(sparseValues, "sparseValues", "sparseToDense", "string_or_numeric");
    const $defaultValue = (0, _tensorUtilEnv.convertToTensor)(defaultValue, "defaultValue", "sparseToDense", $sparseValues.dtype);
    _sparseToDenseUtil.validateInput($sparseIndices, $sparseValues, outputShape, $defaultValue);
    const inputs = {
        sparseIndices: $sparseIndices,
        sparseValues: $sparseValues,
        defaultValue: $defaultValue
    };
    const attrs = {
        outputShape
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.SparseToDense), inputs, attrs);
}
Converts a sparse representation into a dense tensor.

Builds an array dense with shape outputShape such that:

// If sparseIndices is scalar
dense[i] = (i == sparseIndices ? sparseValues : defaultValue)

// If sparseIndices is a vector, then for each i
dense[sparseIndices[i]] = sparseValues[i]

// If sparseIndices is an n by d matrix, then for each i in [0, n)
dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]
All other values in dense are set to defaultValue. If sparseValues is a
scalar, all sparse indices are set to this single value.

If indices are repeated the final value is summed over all values for those
indices.

```js
const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');
const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');
const shape = [8];
tf.sparseToDense(indices, values, shape).print();
```

@param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.
sparseIndices[i] contains the complete index where sparseValues[i] will be
placed.
@param sparseValues A 0-D or 1-D Tensor. Values
corresponding to each row of sparseIndices, or a scalar value to be used for
all sparse indices.
@param outputShape Shape of the dense output tensor. The type is inferred.
@param defaultValue Scalar. Value to set for indices not specified in
sparseIndices. Defaults to zero.

@doc {heading: 'Operations', subheading: 'Normalization'}