docs: update REPL namespace documentation

Signed-off-by: stdlib-bot <[email protected]>

lib/node_modules/@stdlib/repl/code-blocks/data/data.csv

@@ -1786,8 +1786,8 @@ base.strided.dsortsh,"var x = new Float64Array( [ 1.0, -2.0, 3.0, -4.0 ] );\nbas
 base.strided.dsortsh.ndarray,"var x = new Float64Array( [ 1.0, -2.0, 3.0, -4.0 ] );\nbase.strided.dsortsh.ndarray( x.length, 1, x, 1, 0 )\nx = new Float64Array( [ 1.0, -2.0, 3.0, -4.0 ] );\nvar N = base.floor( x.length / 2 );\nbase.strided.dsortsh.ndarray( N, 1, x, 2, 1 )\n"
 base.strided.dsqrt,"var x = new Float64Array( [ 0.0, 4.0, 9.0, 12.0 ] );\nvar y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0 ] );\nbase.strided.dsqrt( x.length, x, 1, y, 1 )\nvar N = base.floor( x.length / 2 );\ny = new Float64Array( [ 0.0, 0.0, 0.0, 0.0 ] );\nbase.strided.dsqrt( N, x, 2, y, -1 )\nvar x0 = new Float64Array( [ 0.0, 4.0, 9.0, 12.0 ] );\nvar y0 = new Float64Array( [ 0.0, 0.0, 0.0, 0.0 ] );\nvar x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\nvar y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*2 );\nN = base.floor( x0.length / 2 );\nbase.strided.dsqrt( N, x1, -2, y1, 1 )\ny0\n"
 base.strided.dsqrt.ndarray,"var x = new Float64Array( [ 0.0, 4.0, 9.0, 12.0 ] );\nvar y = new Float64Array( [ 0.0, 0.0, 0.0, 0.0 ] );\nbase.strided.dsqrt.ndarray( x.length, x, 1, 0, y, 1, 0 )\nx = new Float64Array( [ 0.0, 4.0, 9.0, 12.0 ] );\ny = new Float64Array( [ 0.0, 0.0, 0.0, 0.0 ] );\nvar N = base.floor( x.length / 2 );\nbase.strided.dsqrt.ndarray( N, x, 2, 1, y, -1, y.length-1 )\n"
-base.strided.dssum,"var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\nbase.strided.dssum( x.length, x, 1 )\nx = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\nvar N = base.floor( x.length / 2 );\nvar stride = 2;\nbase.strided.dssum( N, x, stride )\nvar x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\nN = base.floor( x0.length / 2 );\nstride = 2;\nbase.strided.dssum( N, x1, stride )\n"
-base.strided.dssum.ndarray,"var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\nbase.strided.dssum.ndarray( x.length, x, 1, 0 )\nvar x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar N = base.floor( x.length / 2 );\nbase.strided.dssum.ndarray( N, x, 2, 1 )\n"
+base.strided.dssum,"var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\nbase.strided.dssum( x.length, x, 1 )\nx = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\nbase.strided.dssum( 3, x, 2 )\nvar x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\nbase.strided.dssum( 3, x1, 2 )\n"
+base.strided.dssum.ndarray,"var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\nbase.strided.dssum.ndarray( x.length, x, 1, 0 )\nvar x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nbase.strided.dssum.ndarray(3, x, 2, 1 )\n"
 base.strided.dssumors,"var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\nbase.strided.dssumors( x.length, x, 1 )\nx = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\nvar N = base.floor( x.length / 2 );\nvar stride = 2;\nbase.strided.dssumors( N, x, stride )\nvar x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\nN = base.floor( x0.length / 2 );\nstride = 2;\nbase.strided.dssumors( N, x1, stride )\n"
 base.strided.dssumors.ndarray,"var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\nbase.strided.dssumors.ndarray( x.length, x, 1, 0 )\nvar x = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar N = base.floor( x.length / 2 );\nbase.strided.dssumors.ndarray( N, x, 2, 1 )\n"
 base.strided.dssumpw,"var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\nbase.strided.dssumpw( x.length, x, 1 )\nx = new Float32Array( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );\nvar N = base.floor( x.length / 2 );\nvar stride = 2;\nbase.strided.dssumpw( N, x, stride )\nvar x0 = new Float32Array( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );\nvar x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 );\nN = base.floor( x0.length / 2 );\nstride = 2;\nbase.strided.dssumpw( N, x1, stride )\n"