Initial Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

LLM Generation

Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

Output

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import jax
import jax.numpy as jnp

def mlp(input_size, hidden_size, output_size):
    params = {
        'linear1': jax.nn.initializers.he_normal()((input_size, hidden_size)),
        'linear2': jax.nn.initializers.he_normal()((hidden_size, output_size)),
    }
    def apply(params, x):
        x = jnp.dot(x, params['linear1'])
        x = jax.nn.relu(x)
        x = jnp.dot(x, params['linear2'])
        return jax.nn.softmax(x, axis=1)
    return params, apply

model, apply = mlp(10, 20, 30)
x = jnp.array(jnp.random.randn(5, 10))
y = apply(model, x)
print(list(jnp.sum(y, axis=1)))

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp

def mlp(input_size, hidden_size, output_size):
    params = {
        'linear1': jax.nn.initializers.he_normal()((input_size, hidden_size)),
        'linear2': jax.nn.initializers.he_normal()((hidden_size, output_size)),
    }
    def apply(params, x):
        x = jnp.dot(x, params['linear1'])
        x = jax.nn.relu(x)
        x = jnp.dot(x, params['linear2'])
        return jax.nn.softmax(x, axis=1)
    return params, apply

model, apply = mlp(10, 20, 30)
x = jnp.array(jnp.random.randn(5, 10))
y = apply(model, x)
print(list(jnp.sum(y, axis=1)))

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp

def mlp(input_size, hidden_size, output_size):
    params = {
        'linear1': jax.nn.initializers.he_normal()((input_size, hidden_size)),
        'linear2': jax.nn.initializers.he_normal()((hidden_size, output_size)),
    }
    def apply(params, x):
        x = jnp.dot(x, params['linear1'])
        x = jax.nn.relu(x)
        x = jnp.dot(x, params['linear2'])
        return jax.nn.softmax(x, axis=1)
    return params, apply

model, apply = mlp(10, 20, 30)
x = jnp.array(jnp.random.randn(5, 10))
y = apply(model, x)
print(list(jnp.sum(y, axis=1)))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 17, in <module> model, apply = mlp(10, 20, 30) ^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 7, in mlp 'linear1': jax.nn.initializers.he_normal()((input_size, hidden_size)), ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: variance_scaling.<locals>.init() missing 1 required positional argument: 'shape'

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: False

Substring Evaluation

Testing if the previous output contains the string 1.0: False

Substring Evaluation

Testing if the previous output contains the string 1.00000: False

Substring Evaluation

Testing if the previous output contains the string 1.0,: False

Final Answer: False

Check this condition is not true:

Substring Evaluation

Testing if the previous output contains the string import torch: False

Final Answer: True

Final Answer: False

Initial Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

LLM Generation

Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

Output

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import jax
import jax.numpy as jnp

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.linear1 = jax.nn.Dense(input_size, hidden_size)
        self.linear2 = jax.nn.Dense(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = jax.nn.relu(x)
        x = self.linear2(x)
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
x = jax.random.normal(jax.random.PRNGKey(42), (5, 10))
y = model(x)
print(list(jnp.sum(y, axis=1)))

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.linear1 = jax.nn.Dense(input_size, hidden_size)
        self.linear2 = jax.nn.Dense(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = jax.nn.relu(x)
        x = self.linear2(x)
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
x = jax.random.normal(jax.random.PRNGKey(42), (5, 10))
y = model(x)
print(list(jnp.sum(y, axis=1)))

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.linear1 = jax.nn.Dense(input_size, hidden_size)
        self.linear2 = jax.nn.Dense(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = jax.nn.relu(x)
        x = self.linear2(x)
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
x = jax.random.normal(jax.random.PRNGKey(42), (5, 10))
y = model(x)
print(list(jnp.sum(y, axis=1)))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 16, in <module> model = MLP(10, 20, 30) ^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 7, in __init__ self.linear1 = jax.nn.Dense(input_size, hidden_size) ^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/jax/_src/deprecations.py", line 53, in getattr raise AttributeError(f"module {module!r} has no attribute {name!r}") AttributeError: module 'jax.nn' has no attribute 'Dense'

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: False

Substring Evaluation

Testing if the previous output contains the string 1.0: False

Substring Evaluation

Testing if the previous output contains the string 1.00000: False

Substring Evaluation

Testing if the previous output contains the string 1.0,: False

Final Answer: False

Check this condition is not true:

Substring Evaluation

Testing if the previous output contains the string import torch: False

Final Answer: True

Final Answer: False

Initial Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

LLM Generation

Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

Output

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import jax
import jax.numpy as jnp
from jax import jit

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = self.init_params(input_size, hidden_size, output_size)

    def init_params(self, input_size, hidden_size, output_size):
        keys = jax.random.split(jax.random.PRNGKey(42), 2)
        params = {
            'linear1': jax.random.normal(keys[0], (input_size, hidden_size)),
            'linear2': jax.random.normal(keys[1], (hidden_size, output_size)),
        }
        return params

    def __call__(self, x):
        x = jnp.dot(x, self.params['linear1'])
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.params['linear2'])
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
x = jax.random.normal(jax.random.PRNGKey(42), (5, 10))
y = model(x)
print(list(y.flatten().sum(1)))

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp
from jax import jit

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = self.init_params(input_size, hidden_size, output_size)

    def init_params(self, input_size, hidden_size, output_size):
        keys = jax.random.split(jax.random.PRNGKey(42), 2)
        params = {
            'linear1': jax.random.normal(keys[0], (input_size, hidden_size)),
            'linear2': jax.random.normal(keys[1], (hidden_size, output_size)),
        }
        return params

    def __call__(self, x):
        x = jnp.dot(x, self.params['linear1'])
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.params['linear2'])
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
x = jax.random.normal(jax.random.PRNGKey(42), (5, 10))
y = model(x)
print(list(y.flatten().sum(1)))

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp
from jax import jit

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = self.init_params(input_size, hidden_size, output_size)

    def init_params(self, input_size, hidden_size, output_size):
        keys = jax.random.split(jax.random.PRNGKey(42), 2)
        params = {
            'linear1': jax.random.normal(keys[0], (input_size, hidden_size)),
            'linear2': jax.random.normal(keys[1], (hidden_size, output_size)),
        }
        return params

    def __call__(self, x):
        x = jnp.dot(x, self.params['linear1'])
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.params['linear2'])
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
x = jax.random.normal(jax.random.PRNGKey(42), (5, 10))
y = model(x)
print(list(y.flatten().sum(1)))
And got the output: 
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Traceback (most recent call last):
  File "/usr/src/app/main.py", line 27, in <module>
    print(list(y.flatten().sum(1)))
               ^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 226, in sum
    return _reduce_sum(a, axis=_ensure_optional_axes(axis), dtype=dtype, out=out,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 216, in _reduce_sum
    return _reduction(a, "sum", np.sum, lax.add, 0, preproc=_cast_to_numeric,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 96, in _reduction
    pos_dims, dims = _reduction_dims(a, axis)
                     ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 154, in _reduction_dims
    canon_axis = tuple(_canonicalize_axis_allow_named(x, np.ndim(a))
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 154, in <genexpr>
    canon_axis = tuple(_canonicalize_axis_allow_named(x, np.ndim(a))
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 147, in _canonicalize_axis_allow_named
    return maybe_named_axis(x, lambda i: _canonicalize_axis(i, rank), lambda name: name)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/jax/_src/numpy/reductions.py", line 147, in <lambda>
    return maybe_named_axis(x, lambda i: _canonicalize_axis(i, rank), lambda name: name)
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: axis 1 is out of bounds for array of dimension 1</p>
<hr />
<p>For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: False

Substring Evaluation

Testing if the previous output contains the string 1.0: False

Substring Evaluation

Testing if the previous output contains the string 1.00000: False

Substring Evaluation

Testing if the previous output contains the string 1.0,: False

Final Answer: False

Check this condition is not true:

Substring Evaluation

Testing if the previous output contains the string import torch: False

Final Answer: True

Final Answer: False

Initial Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

LLM Generation

Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

Output

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import jax
import jax.numpy as jnp

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.linear1 = jax.nn.Linear(input_size, hidden_size)
        self.linear2 = jax.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = jax.nn.relu(x)
        x = self.linear2(x)
        return jax.nn.softmax(x, axis=-1)

model = MLP(10, 20, 30)
x = jnp.randn(5, 10)
y = model(x)
print(list(jnp.sum(y, axis=1)))

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.linear1 = jax.nn.Linear(input_size, hidden_size)
        self.linear2 = jax.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = jax.nn.relu(x)
        x = self.linear2(x)
        return jax.nn.softmax(x, axis=-1)

model = MLP(10, 20, 30)
x = jnp.randn(5, 10)
y = model(x)
print(list(jnp.sum(y, axis=1)))

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.linear1 = jax.nn.Linear(input_size, hidden_size)
        self.linear2 = jax.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = jax.nn.relu(x)
        x = self.linear2(x)
        return jax.nn.softmax(x, axis=-1)

model = MLP(10, 20, 30)
x = jnp.randn(5, 10)
y = model(x)
print(list(jnp.sum(y, axis=1)))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 16, in <module> model = MLP(10, 20, 30) ^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 7, in __init__ self.linear1 = jax.nn.Linear(input_size, hidden_size) ^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/jax/_src/deprecations.py", line 53, in getattr raise AttributeError(f"module {module!r} has no attribute {name!r}") AttributeError: module 'jax.nn' has no attribute 'Linear'

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: False

Substring Evaluation

Testing if the previous output contains the string 1.0: False

Substring Evaluation

Testing if the previous output contains the string 1.00000: False

Substring Evaluation

Testing if the previous output contains the string 1.0,: False

Final Answer: False

Check this condition is not true:

Substring Evaluation

Testing if the previous output contains the string import torch: False

Final Answer: True

Final Answer: False

Initial Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

LLM Generation

Query

Convert the following PyTorch code to pure JAX. Do not use any other libraries. 

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import torch

class MLP(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_size, hidden_size)
        self.linear2 = torch.nn.Linear(hidden_size, output_size)

    def __call__(self, x):
        x = self.linear1(x)
        x = torch.relu(x)
        x = self.linear2(x)
        return torch.nn.functional.softmax(x, dim=1)

model = MLP(10, 20, 30)
x = torch.randn(5, 10)
y = model(x)
print(list(y.flatten().numpy().sum(1)))

Output

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import jax
import jax.numpy as jnp

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {"W1": jax.random.normal((input_size, hidden_size)),
                       "b1": jax.random.normal((hidden_size, )),
                       "W2": jax.random.normal((hidden_size, output_size)),
                       "b2": jax.random.normal((output_size, ))}

    def __call__(self, x):
        x = jnp.dot(x, self.params["W1"]) + self.params["b1"]
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.params["W2"]) + self.params["b2"]
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
x = jax.random.normal((5, 10))
y = model(x)
print(list(y.flatten().sum(1)))

Extract Code

I extracted the following code from that output: 

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import jax
import jax.numpy as jnp

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {"W1": jax.random.normal((input_size, hidden_size)),
                       "b1": jax.random.normal((hidden_size, )),
                       "W2": jax.random.normal((hidden_size, output_size)),
                       "b2": jax.random.normal((output_size, ))}

    def __call__(self, x):
        x = jnp.dot(x, self.params["W1"]) + self.params["b1"]
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.params["W2"]) + self.params["b2"]
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
x = jax.random.normal((5, 10))
y = model(x)
print(list(y.flatten().sum(1)))

Check if all of the following conditions are true:

Run Code Interpreter

Running the following program: 

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import jax
import jax.numpy as jnp

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.params = {"W1": jax.random.normal((input_size, hidden_size)),
                       "b1": jax.random.normal((hidden_size, )),
                       "W2": jax.random.normal((hidden_size, output_size)),
                       "b2": jax.random.normal((output_size, ))}

    def __call__(self, x):
        x = jnp.dot(x, self.params["W1"]) + self.params["b1"]
        x = jax.nn.relu(x)
        x = jnp.dot(x, self.params["W2"]) + self.params["b2"]
        return jax.nn.softmax(x, axis=1)

model = MLP(10, 20, 30)
x = jax.random.normal((5, 10))
y = model(x)
print(list(y.flatten().sum(1)))
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 18, in <module> model = MLP(10, 20, 30) ^^^^^^^^^^^^^^^ File "/usr/src/app/main.py", line 7, in __init__ self.params = {"W1": jax.random.normal((input_size, hidden_size)), ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/jax/_src/random.py", line 713, in normal key, _ = _check_prng_key("normal", key) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/jax/_src/random.py", line 101, in _check_prng_key raise TypeError(f'unexpected PRNG key type {type(key)}') TypeError: unexpected PRNG key type <class 'tuple'>

Check if any of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string 0.99999: False

Substring Evaluation

Testing if the previous output contains the string 1.0: False

Substring Evaluation

Testing if the previous output contains the string 1.00000: False

Substring Evaluation

Testing if the previous output contains the string 1.0,: False

Final Answer: False

Check this condition is not true:

Substring Evaluation

Testing if the previous output contains the string import torch: False

Final Answer: True

Final Answer: False