Tensorflow.js is an open-source library that is developed by Google for running machine learning models as well as deep learning neural networks in the browser or node environment.
The .fetch() function is used to return a platform dedicated operation of fetch. Moreover, in case fetch is specified in contact with the global object i.e. window, process, and so on, then tf.util.fetch returns that function else returns a platform dedicated explication.
Syntax:
tf.fetch(path, requestInits?)
Parameters:Â
- path: It is the stated path which is of type string.
- requestInits: The stated RequestInit. It is optional and is of type string.
Return Value: It returns the promise of response.
Example 1:
Javascript
// Importing the tensorflow.js library import * as tf from "@tensorflow/tfjs"  // Calling fetch() method with respect // to global const res = tf.env().global.fetch(   // Printing output console.log(res); |
Output:
[object Response]
Example 2:
Javascript
// Importing the tensorflow.js library import * as tf from "@tensorflow/tfjs"Â Â // Calling fetch() method const res = await tf.util.fetch( Â Â // Printing output console.log(JSON.stringify(res)); |
Output:
{}
Reference: https://js.tensorflow.org/api/latest/#fetch
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