[Term Entry] PyTorch Tensor Operations: .fmod()

* Add PyTorch .fmod() term entry

* minor changes

* Minor changes

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Author: BV003

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

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+---
+Title: '.fmod()'
+Description: 'Returns the element-wise remainder of division, keeping the same sign as the dividend.'
+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 **`.fmod()`** method performs element-wise modulo operation on a [tensor](https://www.codecademy.com/resources/docs/pytorch/tensors) and returns a new tensor containing the remainders. The sign of the result always matches the sign of the dividend (the input tensor elements).
+
+Mathematically, for each element in the input tensor, the operation follows:
+
+$$
+\text{result} = \text{input} - (\text{divisor} \times \text{trunc}(\text{input}/\text{divisor}))
+$$
+
+Here, `trunc` means truncation toward zero (i.e., rounding towards 0).
+
+## Syntax
+
+```pseudo
+torch.fmod(input, other, *, out=None) -> Tensor
+```
+
+**Parameters:**
+
+- `input` (Tensor): Dividend tensor.
+- `other` (Tensor or Scalar): Divisor, can be a tensor (broadcastable with input) or a scalar.
+- `out` (Tensor, optional): Output tensor to store the result.
+
+**Return value:**
+
+- Returns a tensor containing the element-wise remainder of division, where the result has the same sign as `input`.
+
+## Example
+
+This example demonstrates the usage of the `.fmod()` function:
+
+```py
+import torch
+
+# Scalar divisor
+x = torch.tensor([5.0, -3.5, 2.1])
+result = x.fmod(2)  # or torch.fmod(x, 2)
+print(result)
+
+# Tensor divisor
+x = torch.tensor([5.0, -3.5, 2.1])
+y = torch.tensor([3.0, 2.0, 1.5])
+result = x.fmod(y)
+print(result)
+
+# In-place operation
+x = torch.tensor([5.0, -3.5, 2.1])
+x.fmod_(2)  # x is modified directly
+print(x)
+```
+
+The output of this code is:
+
+```shell
+tensor([ 1.0000, -1.5000,  0.1000])
+tensor([ 2.0000, -1.5000,  0.6000])
+tensor([ 1.0000, -1.5000,  0.1000])
+```