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tensor

Provide internal nodes for the Arrow C++ backed tensor runtime, aligned with Apache Arrow's homogeneous N-dimensional tensor model.

Classes:

TensorByteOffset 

TensorByteOffset(base: AST, indices: Sequence[AST])

Bases: DataType

Methods:

Source code in packages/irx/src/irx/astx/tensor.py 
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def __init__(
    self,
    base: astx.AST,
    indices: Sequence[astx.AST],
) -> None:
    """
    title: Initialize one Tensor byte-offset query.
    parameters:
      base:
        type: astx.AST
      indices:
        type: Sequence[astx.AST]
    """
    super().__init__()
    if not indices:
        raise ValueError("tensor indexing requires at least one index")
    self.base = base
    self.indices = list(indices)
    self.type_ = astx.Int64()

get_struct 

get_struct(simplified: bool = False) -> ReprStruct
Source code in packages/irx/src/irx/astx/tensor.py 
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def get_struct(self, simplified: bool = False) -> astx.base.ReprStruct:
    """
    title: Return the structured representation of the byte-offset query.
    parameters:
      simplified:
        type: bool
    returns:
      type: astx.base.ReprStruct
    """
    value = {
        "base": self.base.get_struct(simplified),
        "indices": [
            index.get_struct(simplified) for index in self.indices
        ],
    }
    return self._prepare_struct(
        "TensorByteOffset",
        cast(astx.base.ReprStruct, value),
        simplified,
    )

to_json 

to_json(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_json(self, simplified: bool = False) -> str:
    """
    title: Return an json string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return json.dumps(self.get_struct(simplified=simplified), indent=2)

to_yaml 

to_yaml(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_yaml(self, simplified: bool = False) -> str:
    """
    title: Return an yaml string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return str(
        yaml.dump(self.get_struct(simplified=simplified), sort_keys=False)
    )

TensorElementCount 

TensorElementCount(base: AST)

Bases: DataType

Methods:

Source code in packages/irx/src/irx/astx/tensor.py 
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def __init__(self, base: astx.AST) -> None:
    """
    title: Initialize one Tensor element-count query.
    parameters:
      base:
        type: astx.AST
    """
    super().__init__()
    self.base = base
    self.type_ = astx.Int64()

get_struct 

get_struct(simplified: bool = False) -> ReprStruct
Source code in packages/irx/src/irx/astx/tensor.py 
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def get_struct(self, simplified: bool = False) -> astx.base.ReprStruct:
    """
    title: Return the structured representation of the element-count query.
    parameters:
      simplified:
        type: bool
    returns:
      type: astx.base.ReprStruct
    """
    return self._prepare_struct(
        "TensorElementCount",
        self.base.get_struct(simplified),
        simplified,
    )

to_json 

to_json(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_json(self, simplified: bool = False) -> str:
    """
    title: Return an json string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return json.dumps(self.get_struct(simplified=simplified), indent=2)

to_yaml 

to_yaml(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_yaml(self, simplified: bool = False) -> str:
    """
    title: Return an yaml string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return str(
        yaml.dump(self.get_struct(simplified=simplified), sort_keys=False)
    )

TensorIndex 

TensorIndex(base: AST, indices: Sequence[AST])

Bases: DataType

Methods:

Source code in packages/irx/src/irx/astx/tensor.py 
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def __init__(
    self,
    base: astx.AST,
    indices: Sequence[astx.AST],
) -> None:
    """
    title: Initialize one Tensor indexed read.
    parameters:
      base:
        type: astx.AST
      indices:
        type: Sequence[astx.AST]
    """
    super().__init__()
    if not indices:
        raise ValueError("tensor indexing requires at least one index")
    self.base = base
    self.indices = list(indices)
    self.type_ = AnyType()

get_struct 

get_struct(simplified: bool = False) -> ReprStruct
Source code in packages/irx/src/irx/astx/tensor.py 
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def get_struct(self, simplified: bool = False) -> astx.base.ReprStruct:
    """
    title: Return the structured representation of the indexed read.
    parameters:
      simplified:
        type: bool
    returns:
      type: astx.base.ReprStruct
    """
    value = {
        "base": self.base.get_struct(simplified),
        "indices": [
            index.get_struct(simplified) for index in self.indices
        ],
    }
    return self._prepare_struct(
        "TensorIndex",
        cast(astx.base.ReprStruct, value),
        simplified,
    )

to_json 

to_json(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_json(self, simplified: bool = False) -> str:
    """
    title: Return an json string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return json.dumps(self.get_struct(simplified=simplified), indent=2)

to_yaml 

to_yaml(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_yaml(self, simplified: bool = False) -> str:
    """
    title: Return an yaml string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return str(
        yaml.dump(self.get_struct(simplified=simplified), sort_keys=False)
    )

TensorLiteral 

TensorLiteral(
    values: Sequence[AST],
    *,
    element_type: DataType,
    shape: Sequence[int],
    strides: Sequence[int] | None = None,
    offset_bytes: int = 0,
)

Bases: DataType

Build one Arrow C++ tensor value from scalar values plus shape and stride metadata. attributes: values: type: list[astx.AST] element_type: type: astx.DataType shape: type: tuple[int, Ellipsis] strides: type: tuple[int, Ellipsis] | None offset_bytes: type: int type_: type: TensorType

Methods:

Source code in packages/irx/src/irx/astx/tensor.py 
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def __init__(
    self,
    values: Sequence[astx.AST],
    *,
    element_type: astx.DataType,
    shape: Sequence[int],
    strides: Sequence[int] | None = None,
    offset_bytes: int = 0,
) -> None:
    """
    title: Initialize one Tensor literal.
    parameters:
      values:
        type: Sequence[astx.AST]
      element_type:
        type: astx.DataType
      shape:
        type: Sequence[int]
      strides:
        type: Sequence[int] | None
      offset_bytes:
        type: int
    """
    super().__init__()
    self.values = list(values)
    self.element_type = element_type
    self.shape = tuple(shape)
    self.strides = None if strides is None else tuple(strides)
    self.offset_bytes = offset_bytes
    self.type_ = TensorType(element_type)

get_struct 

get_struct(simplified: bool = False) -> ReprStruct
Source code in packages/irx/src/irx/astx/tensor.py 
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def get_struct(self, simplified: bool = False) -> astx.base.ReprStruct:
    """
    title: Return the structured representation of the Tensor literal.
    parameters:
      simplified:
        type: bool
    returns:
      type: astx.base.ReprStruct
    """
    value = {
        "values": [item.get_struct(simplified) for item in self.values],
        "element_type": self.element_type.get_struct(simplified),
        "shape": list(self.shape),
        "strides": (None if self.strides is None else list(self.strides)),
        "offset_bytes": self.offset_bytes,
    }
    return self._prepare_struct(
        "TensorLiteral",
        cast(astx.base.ReprStruct, value),
        simplified,
    )

to_json 

to_json(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_json(self, simplified: bool = False) -> str:
    """
    title: Return an json string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return json.dumps(self.get_struct(simplified=simplified), indent=2)

to_yaml 

to_yaml(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_yaml(self, simplified: bool = False) -> str:
    """
    title: Return an yaml string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return str(
        yaml.dump(self.get_struct(simplified=simplified), sort_keys=False)
    )

TensorNDim 

TensorNDim(base: AST)

Bases: DataType

Methods:

Source code in packages/irx/src/irx/astx/tensor.py 
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def __init__(self, base: astx.AST) -> None:
    """
    title: Initialize one Tensor rank query.
    parameters:
      base:
        type: astx.AST
    """
    super().__init__()
    self.base = base
    self.type_ = astx.Int32()

get_struct 

get_struct(simplified: bool = False) -> ReprStruct
Source code in packages/irx/src/irx/astx/tensor.py 
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def get_struct(self, simplified: bool = False) -> astx.base.ReprStruct:
    """
    title: Return the structured representation of the rank query.
    parameters:
      simplified:
        type: bool
    returns:
      type: astx.base.ReprStruct
    """
    return self._prepare_struct(
        "TensorNDim",
        self.base.get_struct(simplified),
        simplified,
    )

to_json 

to_json(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_json(self, simplified: bool = False) -> str:
    """
    title: Return an json string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return json.dumps(self.get_struct(simplified=simplified), indent=2)

to_yaml 

to_yaml(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_yaml(self, simplified: bool = False) -> str:
    """
    title: Return an yaml string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return str(
        yaml.dump(self.get_struct(simplified=simplified), sort_keys=False)
    )

TensorRelease 

TensorRelease(base: AST)

Bases: DataType

Methods:

Source code in packages/irx/src/irx/astx/tensor.py 
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def __init__(self, base: astx.AST) -> None:
    """
    title: Initialize one Tensor release helper.
    parameters:
      base:
        type: astx.AST
    """
    super().__init__()
    self.base = base
    self.type_ = astx.Int32()

get_struct 

get_struct(simplified: bool = False) -> ReprStruct
Source code in packages/irx/src/irx/astx/tensor.py 
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def get_struct(self, simplified: bool = False) -> astx.base.ReprStruct:
    """
    title: Return the structured representation of the release helper.
    parameters:
      simplified:
        type: bool
    returns:
      type: astx.base.ReprStruct
    """
    return self._prepare_struct(
        "TensorRelease",
        self.base.get_struct(simplified),
        simplified,
    )

to_json 

to_json(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_json(self, simplified: bool = False) -> str:
    """
    title: Return an json string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return json.dumps(self.get_struct(simplified=simplified), indent=2)

to_yaml 

to_yaml(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_yaml(self, simplified: bool = False) -> str:
    """
    title: Return an yaml string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return str(
        yaml.dump(self.get_struct(simplified=simplified), sort_keys=False)
    )

TensorRetain 

TensorRetain(base: AST)

Bases: DataType

Methods:

Source code in packages/irx/src/irx/astx/tensor.py 
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def __init__(self, base: astx.AST) -> None:
    """
    title: Initialize one Tensor retain helper.
    parameters:
      base:
        type: astx.AST
    """
    super().__init__()
    self.base = base
    self.type_ = astx.Int32()

get_struct 

get_struct(simplified: bool = False) -> ReprStruct
Source code in packages/irx/src/irx/astx/tensor.py 
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def get_struct(self, simplified: bool = False) -> astx.base.ReprStruct:
    """
    title: Return the structured representation of the retain helper.
    parameters:
      simplified:
        type: bool
    returns:
      type: astx.base.ReprStruct
    """
    return self._prepare_struct(
        "TensorRetain",
        self.base.get_struct(simplified),
        simplified,
    )

to_json 

to_json(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_json(self, simplified: bool = False) -> str:
    """
    title: Return an json string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return json.dumps(self.get_struct(simplified=simplified), indent=2)

to_yaml 

to_yaml(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_yaml(self, simplified: bool = False) -> str:
    """
    title: Return an yaml string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return str(
        yaml.dump(self.get_struct(simplified=simplified), sort_keys=False)
    )

TensorShape 

TensorShape(base: AST, axis: int)

Bases: DataType

Methods:

Source code in packages/irx/src/irx/astx/tensor.py 
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def __init__(self, base: astx.AST, axis: int) -> None:
    """
    title: Initialize one Tensor shape query.
    parameters:
      base:
        type: astx.AST
      axis:
        type: int
    """
    super().__init__()
    self.base = base
    self.axis = axis
    self.type_ = astx.Int64()

get_struct 

get_struct(simplified: bool = False) -> ReprStruct
Source code in packages/irx/src/irx/astx/tensor.py 
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def get_struct(self, simplified: bool = False) -> astx.base.ReprStruct:
    """
    title: Return the structured representation of the shape query.
    parameters:
      simplified:
        type: bool
    returns:
      type: astx.base.ReprStruct
    """
    value = {
        "base": self.base.get_struct(simplified),
        "axis": self.axis,
    }
    return self._prepare_struct(
        "TensorShape",
        cast(astx.base.ReprStruct, value),
        simplified,
    )

to_json 

to_json(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_json(self, simplified: bool = False) -> str:
    """
    title: Return an json string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return json.dumps(self.get_struct(simplified=simplified), indent=2)

to_yaml 

to_yaml(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_yaml(self, simplified: bool = False) -> str:
    """
    title: Return an yaml string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return str(
        yaml.dump(self.get_struct(simplified=simplified), sort_keys=False)
    )

TensorStore 

TensorStore(base: AST, indices: Sequence[AST], value: AST)

Bases: DataType

Stores one scalar through tensor shape and stride metadata. Arrow C++ backed tensors remain readonly in this phase, but the node keeps the surface aligned with future writable views. attributes: base: type: astx.AST indices: type: list[astx.AST] value: type: astx.AST type_: type: astx.Int32

Methods:

Source code in packages/irx/src/irx/astx/tensor.py 
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def __init__(
    self,
    base: astx.AST,
    indices: Sequence[astx.AST],
    value: astx.AST,
) -> None:
    """
    title: Initialize one Tensor indexed store.
    parameters:
      base:
        type: astx.AST
      indices:
        type: Sequence[astx.AST]
      value:
        type: astx.AST
    """
    super().__init__()
    if not indices:
        raise ValueError("tensor indexing requires at least one index")
    self.base = base
    self.indices = list(indices)
    self.value = value
    self.type_ = astx.Int32()

get_struct 

get_struct(simplified: bool = False) -> ReprStruct
Source code in packages/irx/src/irx/astx/tensor.py 
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def get_struct(self, simplified: bool = False) -> astx.base.ReprStruct:
    """
    title: Return the structured representation of the indexed store.
    parameters:
      simplified:
        type: bool
    returns:
      type: astx.base.ReprStruct
    """
    value = {
        "base": self.base.get_struct(simplified),
        "indices": [
            index.get_struct(simplified) for index in self.indices
        ],
        "value": self.value.get_struct(simplified),
    }
    return self._prepare_struct(
        "TensorStore",
        cast(astx.base.ReprStruct, value),
        simplified,
    )

to_json 

to_json(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_json(self, simplified: bool = False) -> str:
    """
    title: Return an json string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return json.dumps(self.get_struct(simplified=simplified), indent=2)

to_yaml 

to_yaml(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_yaml(self, simplified: bool = False) -> str:
    """
    title: Return an yaml string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return str(
        yaml.dump(self.get_struct(simplified=simplified), sort_keys=False)
    )

TensorStride 

TensorStride(base: AST, axis: int)

Bases: DataType

Methods:

Source code in packages/irx/src/irx/astx/tensor.py 
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def __init__(self, base: astx.AST, axis: int) -> None:
    """
    title: Initialize one Tensor stride query.
    parameters:
      base:
        type: astx.AST
      axis:
        type: int
    """
    super().__init__()
    self.base = base
    self.axis = axis
    self.type_ = astx.Int64()

get_struct 

get_struct(simplified: bool = False) -> ReprStruct
Source code in packages/irx/src/irx/astx/tensor.py 
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def get_struct(self, simplified: bool = False) -> astx.base.ReprStruct:
    """
    title: Return the structured representation of the stride query.
    parameters:
      simplified:
        type: bool
    returns:
      type: astx.base.ReprStruct
    """
    value = {
        "base": self.base.get_struct(simplified),
        "axis": self.axis,
    }
    return self._prepare_struct(
        "TensorStride",
        cast(astx.base.ReprStruct, value),
        simplified,
    )

to_json 

to_json(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_json(self, simplified: bool = False) -> str:
    """
    title: Return an json string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return json.dumps(self.get_struct(simplified=simplified), indent=2)

to_yaml 

to_yaml(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_yaml(self, simplified: bool = False) -> str:
    """
    title: Return an yaml string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return str(
        yaml.dump(self.get_struct(simplified=simplified), sort_keys=False)
    )

TensorType 

TensorType(element_type: DataType | None = None)

Bases: AnyType

Represent the homogeneous N-dimensional tensor abstraction while reusing the canonical buffer/view representation during lowering. attributes: element_type: type: astx.DataType | None

Methods:

Source code in packages/irx/src/irx/astx/tensor.py 
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def __init__(self, element_type: astx.DataType | None = None) -> None:
    """
    title: Initialize one Tensor type.
    parameters:
      element_type:
        type: astx.DataType | None
    """
    super().__init__()
    self.element_type = element_type

get_struct 

get_struct(simplified: bool = False) -> ReprStruct
Source code in packages/astx/src/astx/base.py 
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def get_struct(self, simplified: bool = False) -> ReprStruct:
    """
    title: Return a simple structure that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: ReprStruct
    """
    key = f"DATA-TYPE[{self.__class__.__name__}]"
    value = self.name
    return self._prepare_struct(key, value, simplified)

to_json 

to_json(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_json(self, simplified: bool = False) -> str:
    """
    title: Return an json string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return json.dumps(self.get_struct(simplified=simplified), indent=2)

to_yaml 

to_yaml(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_yaml(self, simplified: bool = False) -> str:
    """
    title: Return an yaml string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return str(
        yaml.dump(self.get_struct(simplified=simplified), sort_keys=False)
    )

TensorView 

TensorView(
    base: AST,
    *,
    shape: Sequence[int],
    strides: Sequence[int] | None = None,
    offset_bytes: int = 0,
)

Bases: DataType

Build one shallow tensor view by reusing the base storage and replacing the logical shape, strides, and offset metadata. attributes: base: type: astx.AST shape: type: tuple[int, Ellipsis] strides: type: tuple[int, Ellipsis] | None offset_bytes: type: int type_: type: TensorType

Methods:

Source code in packages/irx/src/irx/astx/tensor.py 
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def __init__(
    self,
    base: astx.AST,
    *,
    shape: Sequence[int],
    strides: Sequence[int] | None = None,
    offset_bytes: int = 0,
) -> None:
    """
    title: Initialize one Tensor view.
    parameters:
      base:
        type: astx.AST
      shape:
        type: Sequence[int]
      strides:
        type: Sequence[int] | None
      offset_bytes:
        type: int
    """
    super().__init__()
    self.base = base
    self.shape = tuple(shape)
    self.strides = None if strides is None else tuple(strides)
    self.offset_bytes = offset_bytes
    self.type_ = TensorType()

get_struct 

get_struct(simplified: bool = False) -> ReprStruct
Source code in packages/irx/src/irx/astx/tensor.py 
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def get_struct(self, simplified: bool = False) -> astx.base.ReprStruct:
    """
    title: Return the structured representation of the Tensor view.
    parameters:
      simplified:
        type: bool
    returns:
      type: astx.base.ReprStruct
    """
    value = {
        "base": self.base.get_struct(simplified),
        "shape": list(self.shape),
        "strides": (None if self.strides is None else list(self.strides)),
        "offset_bytes": self.offset_bytes,
    }
    return self._prepare_struct(
        "TensorView",
        cast(astx.base.ReprStruct, value),
        simplified,
    )

to_json 

to_json(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_json(self, simplified: bool = False) -> str:
    """
    title: Return an json string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return json.dumps(self.get_struct(simplified=simplified), indent=2)

to_yaml 

to_yaml(simplified: bool = False) -> str
Source code in packages/astx/src/astx/base.py 
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def to_yaml(self, simplified: bool = False) -> str:
    """
    title: Return an yaml string that represents the object.
    parameters:
      simplified:
        type: bool
    returns:
      type: str
    """
    return str(
        yaml.dump(self.get_struct(simplified=simplified), sort_keys=False)
    )