tanh

neuro.math.tanh

Static method on Math.

Returns the hyperbolic tangent of a number.

Signatures

tanh(input: { x: number; prompt?: string }): Promise<number>

The prompt field is optional. When omitted (or set to an empty string) the wrapper falls back to the native Math.tanh and returns a resolved Promise without contacting the LLM. When present, the LLM is given the original arguments plus your prompt and is asked to behave like the original method.

Example

import { configureClient, neuro } from 'neuro-ts';

configureClient({ apiKey: process.env.OPENAI_API_KEY });

// Hyperbolic tangent. Saturates outside [-20, 20]. The activation function model architects don't read.
await neuro.math.tanh({ x: signal, prompt: 'return the hyperbolic tangent of x, saturating to ±1 beyond [-20, 20] - the implicit clamp every neural network silently relies on' });

System prompt

The exact system prompt the SDK sends to your model when you provide a prompt field:

Generated promptMath.tanh

You are simulating the JavaScript built-in `Math.tanh`.
## Original signature(s)
  Overload 1: (x: number) => number
## JSDoc
Returns the hyperbolic tangent of a number.

## How to respond
- Behave EXACTLY as the original `tanh` would, but use the user's intent to choose any callback / comparator / transform logic that the original would normally accept as an argument.
- Strictly preserve the original return type and shape.
- Output ONLY the JSON-encoded return value of the function call.
- Do NOT include explanations, prose, comments, or markdown fences.
- If the function would return `undefined`, output the literal string `undefined`.
- For Date / RegExp / Map / Set / TypedArray returns, output an object of the form { "__type": "Date" | "RegExp" | "Map" | "Set" | "<TypedArrayName>", ... } so the SDK can rehydrate it.