[Term Entry] PyTorch lerp() entry (#7615)

* init: creation of log10 and init metadata

* docs: syntax and example

* fix: tensor reference link

* docs: pytorch lerp

* minor wording fixes

* fix: deleting log10.md from lerp branch

* fix: deleting log10.md from lerp branch and minor wording issues

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Author: alessandrocapialbi

content/pytorch/concepts/tensor-operations/terms/lerp/lerp.md

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+---
+Title: '.lerp()'
+Description: 'Returns a tensor containing the linear interpolation of two tensors, controlled by a scalar or tensor weight.'
+Subjects:
+  - 'Computer Science'
+  - 'Machine Learning'
+Tags:
+  - 'Functions'
+  - 'Machine Learning'
+  - 'Python'
+  - 'Tensor'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/computer-science'
+---
+
+In PyTorch, the **`.lerp()`** function computes the linear interpolation between an input [tensor](https://www.codecademy.com/resources/docs/pytorch/tensors) (`input`) and an end tensor (`end`), using a scalar or tensor `weight`. This is mathematically equivalent to applying the function $out_i = start_i + weight_i * (end_i - start_i)$.
+
+The shapes of `input`, `end`, and `weight` must be [broadcastable](https://www.codecademy.com/resources/docs/numpy/array-broadcasting).
+
+## Syntax
+
+```pseudo
+torch.lerp(input, end, weight, *, out=None)
+```
+
+**Parameters:**
+
+- `input`: The input tensor containing the initial points.
+- `end`: The ending tensor containing the finishing points.
+- `weight`: The shapes of input, end, and weight must be [broadcastable](https://www.codecademy.com/resources/docs/numpy/array-broadcasting).
+- `out` (optional): A tensor to store the output. If provided, the result is written to this tensor.
+
+**Return value:**
+
+Returns a new tensor containing the result given by the interpolation formula.
+
+## Example
+
+The following example shows how to compute the interpolation between two tensors using `torch.lerp()` with a float scalar weight:
+
+```py
+import torch
+import math
+
+# Define two tensors
+start = torch.tensor([12.0 , 14.0 , 16.0 , math.log(2.)])
+end = torch.tensor([11.0 , 13.0 , 15.0 , math.log(2.)])
+
+# Compute the interpolation with a float weight
+out = torch.lerp(start, end, 0.8)
+
+print(out)
+```
+
+Here is the output:
+
+```shell
+tensor([11.2000, 13.2000, 15.2000,  0.6931])
+```