diff --git a/syft/tensor.py b/syft/tensor.py
index f9eff20a64..ee9cf8715d 100644
--- a/syft/tensor.py
+++ b/syft/tensor.py
@@ -518,3 +518,41 @@ class TensorBase(object):
         if self.encrypted:
             return NotImplemented
         self.data = 1 / np.array(self.data)
+
+    def log(self):
+        """performs elementwise logarithm operation
+        and returns a new Tensor"""
+        if self.encrypted:
+            return NotImplemented
+        out = np.log(self.data)
+        return TensorBase(out)
+
+    def log_(self):
+        """performs elementwise logarithm operation inplace"""
+        if self.encrypted:
+            return NotImplemented
+        self.data = np.log(self.data)
+        return self
+
+    def log1p(self):
+        """performs elementwise log(1+x) operation
+        and returns new tensor"""
+        if self.encrypted:
+            return NotImplemented
+        out = np.log1p(self.data)
+        return TensorBase(out)
+
+    def log1p_(self):
+        """performs elementwise log(1+x) operation inplace"""
+        if self.encrypted:
+            return NotImplemented
+        self.data = np.log1p(self.data)
+        return self
+
+    def log_normal_(self, mean=0, stdev=1.0):
+        """Fills give tensor with samples from a lognormal distribution
+        with given mean and stdev"""
+        if self.encrypted:
+            return NotImplemented
+        self.data = np.random.lognormal(mean, stdev, self.shape())
+        return self

Implement Default log Functionality for Base Tensor Type
**User Story A:** As a Data Scientist using Syft's Base Tensor type, we want to implement a default method for computing operations on a Tensor of arbitrary type. For this ticket to be complete, log(), log1p(), and log_normal() should return a new tensor and log_(), log1p_(), and log_normal_() should perform the operation inline. For a reference on the operation this performs check out [PyTorch](http://pytorch.org/docs/master/tensors.html)'s documentation.

**Acceptance Criteria:**
- If the Base Tensor type's attribute "encrypted" is set to True, it should return a NotImplemented error.
- a unit test demonstrating the correct operation on the Base Tensor type implemented over int and float Tensors.
- inline documentation in the python code. For inspiration on inline documentation, please check out [PyTorch](http://pytorch.org/docs/master/tensors.html)'s documentation for this operator.
diff --git a/tests/test_tensor.py b/tests/test_tensor.py
index 1ae53eb8ce..d73c443827 100644
--- a/tests/test_tensor.py
+++ b/tests/test_tensor.py
@@ -2,6 +2,7 @@ from syft import TensorBase
 import syft
 import unittest
 import numpy as np
+import math
 
 
 # Here's our "unit tests".
@@ -374,3 +375,21 @@ class reciprocalTests(unittest.TestCase):
         t1 = TensorBase(np.array([2, 3, 4]))
         t1.reciprocal_()
         self.assertTrue(np.allclose(t1.data, [0.5, 0.33333333, 0.25]))
+
+
+class logTests(unittest.TestCase):
+    def testLog(self):
+        t1 = TensorBase(np.array([math.exp(1), math.exp(2), math.exp(3)]))
+        self.assertTrue(np.array_equal((t1.log()).data, [1., 2., 3.]))
+
+    def testLog_(self):
+        t1 = TensorBase(np.array([math.exp(1), math.exp(2), math.exp(3)]))
+        self.assertTrue(np.array_equal((t1.log_()).data, [1., 2., 3.]))
+
+    def testLog1p(self):
+        t1 = TensorBase(np.array([1, 2, 3]))
+        self.assertTrue(np.allclose((t1.log1p()).data, [0.69314718, 1.09861229, 1.38629436]))
+
+    def testLog1p_(self):
+        t1 = TensorBase(np.array([1, 2, 3]))
+        self.assertTrue(np.allclose((t1.log1p_()).data, [0.69314718, 1.09861229, 1.38629436]))

pip install -e .
parse_log_pytest
requirements.txt
apt-get update apt-get install -y gcc
3.9
requirements.txt
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
args==0.1.0
clint==0.5.1
exceptiongroup==1.2.2
flake8==7.2.0
iniconfig==2.1.0
line_profiler==4.2.0
mccabe==0.7.0
numpy==1.26.4
packaging==24.2
phe==1.5.0
pluggy==1.5.0
pycodestyle==2.13.0
pyflakes==3.3.1
pyRserve==1.0.4
pytest==8.3.5
pytest-flake8==1.3.0
-e git+https://github.com/OpenMined/PySyft.git@a81fde47c4593a787f9bb61dd655af58497bef4a#egg=syft
tomli==2.2.1

name: PySyft
channels:
  - defaults
  - https://repo.anaconda.com/pkgs/main
  - https://repo.anaconda.com/pkgs/r
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - ca-certificates=2025.2.25=h06a4308_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.4.4=h6a678d5_1
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - ncurses=6.4=h6a678d5_0
  - openssl=3.0.16=h5eee18b_0
  - pip=25.0=py39h06a4308_0
  - python=3.9.21=he870216_1
  - readline=8.2=h5eee18b_0
  - setuptools=75.8.0=py39h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - tzdata=2025a=h04d1e81_0
  - wheel=0.45.1=py39h06a4308_0
  - xz=5.6.4=h5eee18b_1
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - args==0.1.0
      - clint==0.5.1
      - exceptiongroup==1.2.2
      - flake8==7.2.0
      - iniconfig==2.1.0
      - line-profiler==4.2.0
      - mccabe==0.7.0
      - numpy==1.26.4
      - packaging==24.2
      - phe==1.5.0
      - pluggy==1.5.0
      - pycodestyle==2.13.0
      - pyflakes==3.3.1
      - pyrserve==1.0.4
      - pytest==8.3.5
      - pytest-flake8==1.3.0
      - tomli==2.2.1
prefix: /opt/conda/envs/PySyft

tests/test_tensor.py::logTests::testLog tests/test_tensor.py::logTests::testLog1p tests/test_tensor.py::logTests::testLog1p_ tests/test_tensor.py::logTests::testLog_
tests/test_tensor.py::DimTests::testDimOne tests/test_tensor.py::AddTests::testInplace tests/test_tensor.py::AddTests::testScalar tests/test_tensor.py::AddTests::testSimple tests/test_tensor.py::CeilTests::testCeil_ tests/test_tensor.py::FloorTests::testFloor_ tests/test_tensor.py::SubTests::testInplace tests/test_tensor.py::SubTests::testScalar tests/test_tensor.py::SubTests::testSimple tests/test_tensor.py::MultTests::testInplace tests/test_tensor.py::MultTests::testScalar tests/test_tensor.py::MultTests::testSimple tests/test_tensor.py::DivTests::testInplace tests/test_tensor.py::DivTests::testScalar tests/test_tensor.py::DivTests::testSimple tests/test_tensor.py::AbsTests::testabs tests/test_tensor.py::AbsTests::testabs_ tests/test_tensor.py::ShapeTests::testShape tests/test_tensor.py::SqrtTests::testSqrt tests/test_tensor.py::SqrtTests::testSqrt_ tests/test_tensor.py::SumTests::testDimIsNotNoneInt tests/test_tensor.py::SumTests::testDimNoneInt tests/test_tensor.py::EqualTests::testEqOp tests/test_tensor.py::EqualTests::testEqual tests/test_tensor.py::EqualTests::testIneqOp tests/test_tensor.py::EqualTests::testNotEqual tests/test_tensor.py::IndexTests::testIndexing tests/test_tensor.py::sigmoidTests::testSigmoid tests/test_tensor.py::addmm::testaddmm1d tests/test_tensor.py::addmm::testaddmm2d tests/test_tensor.py::addmm::testaddmm_1d tests/test_tensor.py::addmm::testaddmm_2d tests/test_tensor.py::addcmulTests::testaddcmul1d tests/test_tensor.py::addcmulTests::testaddcmul2d tests/test_tensor.py::addcmulTests::testaddcmul_1d tests/test_tensor.py::addcmulTests::testaddcmul_2d tests/test_tensor.py::addcdivTests::testaddcdiv1d tests/test_tensor.py::addcdivTests::testaddcdiv2d tests/test_tensor.py::addcdivTests::testaddcdiv_1d tests/test_tensor.py::addcdivTests::testaddcdiv_2d tests/test_tensor.py::addmvTests::testaddmv tests/test_tensor.py::addmvTests::testaddmv_ tests/test_tensor.py::addbmmTests::testaddbmm tests/test_tensor.py::addbmmTests::testaddbmm_ tests/test_tensor.py::baddbmmTests::testbaddbmm tests/test_tensor.py::baddbmmTests::testbaddbmm_ tests/test_tensor.py::unsqueezeTests::testUnsqueeze tests/test_tensor.py::unsqueezeTests::testUnsqueeze_ tests/test_tensor.py::expTests::testexp tests/test_tensor.py::expTests::testexp_ tests/test_tensor.py::fracTests::testfrac tests/test_tensor.py::fracTests::testfrac_ tests/test_tensor.py::rsqrtTests::testrsqrt tests/test_tensor.py::rsqrtTests::testrsqrt_ tests/test_tensor.py::numpyTests::testnumpy tests/test_tensor.py::reciprocalTests::testreciprocal tests/test_tensor.py::reciprocalTests::testrsqrt_
syft/tensor.py
syft/tensor.py