# tensorflow.math.special.expint() function in Python

• Last Updated : 11 Aug, 2021

TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.

## expint() function

expint() is used to compute element wise Exponential integral of x. It is defines as the integral of exp(t) / t from -inf to x, with the domain of definition all positive real numbers.

Syntax: tensorflow.math.special.expint( x, name)

Parameter:

• x: It’s a Tensor or Sparse Tensor. Allowed dtypes are float32 and float64.
• name(optional): It defines name for the operation.

Returns: It returns a Tensor of same dtype as x.

Example 1:

## Python3

 `# importing the library``import` `tensorflow as tf` `# Initializing the input tensor``a ``=` `tf.constant([ [``-``5``, ``-``7``],[ ``2``, ``0``]], dtype``=``tf.float64)` `# Printing the input tensor``print``(``'a: '``, a)` `# Calculating result``res ``=` `tf.math.special.expint(a)` `# Printing the result``print``(``'Result: '``, res)`

Output:

```a:  tf.Tensor(
[[-5. -7.]
[ 2.  0.]], shape=(2, 2), dtype=float64)
Result:  tf.Tensor(
[[       nan        nan]
[4.95423436       -inf]], shape=(2, 2), dtype=float64)```

Example 2:

## Python3

 `# importing the library``import` `tensorflow as tf` `# Initializing the input tensor``a ``=` `tf.constant([``1``, ``2``, ``3``, ``4``, ``5``], dtype``=``tf.float64)` `# Printing the input tensor``print``(``'a: '``, a)` `# Calculating result``res ``=` `tf.math.special.expint(a)` `# Printing the result``print``(``'Result: '``, res)`

Output:

a:  tf.Tensor([1. 2. 3. 4. 5.], shape=(5,), dtype=float64)
Result:  tf.Tensor([ 1.89511782  4.95423436  9.93383257 19.63087447 40.18527536], shape=(5,), dtype=float64)

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